Overview

Brought to you by YData

Dataset statistics

Number of variables93
Number of observations300089
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory212.9 MiB
Average record size in memory744.0 B

Variable types

Numeric10
DateTime1
Categorical82

Alerts

city is highly overall correlated with city_Babahoyo and 44 other fieldsHigh correlation
city_Babahoyo is highly overall correlated with city and 2 other fieldsHigh correlation
city_Cayambe is highly overall correlated with cityHigh correlation
city_Cuenca is highly overall correlated with city and 4 other fieldsHigh correlation
city_Daule is highly overall correlated with cityHigh correlation
city_El Carmen is highly overall correlated with city and 2 other fieldsHigh correlation
city_Esmeraldas is highly overall correlated with city and 2 other fieldsHigh correlation
city_Guaranda is highly overall correlated with city and 2 other fieldsHigh correlation
city_Guayaquil is highly overall correlated with city and 3 other fieldsHigh correlation
city_Ibarra is highly overall correlated with city and 2 other fieldsHigh correlation
city_Latacunga is highly overall correlated with city and 3 other fieldsHigh correlation
city_Libertad is highly overall correlated with cityHigh correlation
city_Loja is highly overall correlated with city and 2 other fieldsHigh correlation
city_Machala is highly overall correlated with city and 3 other fieldsHigh correlation
city_Manta is highly overall correlated with city and 3 other fieldsHigh correlation
city_Playas is highly overall correlated with cityHigh correlation
city_Puyo is highly overall correlated with city and 2 other fieldsHigh correlation
city_Quevedo is highly overall correlated with city and 2 other fieldsHigh correlation
city_Quito is highly overall correlated with city and 5 other fieldsHigh correlation
city_Riobamba is highly overall correlated with city and 2 other fieldsHigh correlation
city_Salinas is highly overall correlated with city and 2 other fieldsHigh correlation
city_Santo Domingo is highly overall correlated with city and 2 other fieldsHigh correlation
cluster is highly overall correlated with city and 11 other fieldsHigh correlation
cluster.1 is highly overall correlated with city and 11 other fieldsHigh correlation
days_after_earthquake is highly overall correlated with idHigh correlation
days_from_previous_payday is highly overall correlated with days_to_next_payday and 1 other fieldsHigh correlation
days_to_next_payday is highly overall correlated with days_from_previous_payday and 1 other fieldsHigh correlation
dcoilwtico is highly overall correlated with idHigh correlation
family is highly overall correlated with family_BABY CARE and 31 other fieldsHigh correlation
family_BABY CARE is highly overall correlated with familyHigh correlation
family_BEAUTY is highly overall correlated with familyHigh correlation
family_BEVERAGES is highly overall correlated with familyHigh correlation
family_BOOKS is highly overall correlated with familyHigh correlation
family_BREAD/BAKERY is highly overall correlated with familyHigh correlation
family_CELEBRATION is highly overall correlated with familyHigh correlation
family_CLEANING is highly overall correlated with familyHigh correlation
family_DAIRY is highly overall correlated with familyHigh correlation
family_DELI is highly overall correlated with familyHigh correlation
family_EGGS is highly overall correlated with familyHigh correlation
family_FROZEN FOODS is highly overall correlated with familyHigh correlation
family_GROCERY I is highly overall correlated with familyHigh correlation
family_GROCERY II is highly overall correlated with familyHigh correlation
family_HARDWARE is highly overall correlated with familyHigh correlation
family_HOME AND KITCHEN I is highly overall correlated with familyHigh correlation
family_HOME AND KITCHEN II is highly overall correlated with familyHigh correlation
family_HOME APPLIANCES is highly overall correlated with familyHigh correlation
family_HOME CARE is highly overall correlated with familyHigh correlation
family_LADIESWEAR is highly overall correlated with familyHigh correlation
family_LAWN AND GARDEN is highly overall correlated with familyHigh correlation
family_LINGERIE is highly overall correlated with familyHigh correlation
family_LIQUOR,WINE,BEER is highly overall correlated with familyHigh correlation
family_MAGAZINES is highly overall correlated with familyHigh correlation
family_MEATS is highly overall correlated with familyHigh correlation
family_PERSONAL CARE is highly overall correlated with familyHigh correlation
family_PET SUPPLIES is highly overall correlated with familyHigh correlation
family_PLAYERS AND ELECTRONICS is highly overall correlated with familyHigh correlation
family_POULTRY is highly overall correlated with familyHigh correlation
family_PREPARED FOODS is highly overall correlated with familyHigh correlation
family_PRODUCE is highly overall correlated with familyHigh correlation
family_SCHOOL AND OFFICE SUPPLIES is highly overall correlated with familyHigh correlation
family_SEAFOOD is highly overall correlated with familyHigh correlation
id is highly overall correlated with days_after_earthquake and 1 other fieldsHigh correlation
is_payday is highly overall correlated with days_from_previous_payday and 1 other fieldsHigh correlation
onpromotion is highly overall correlated with salesHigh correlation
sales is highly overall correlated with onpromotionHigh correlation
state is highly overall correlated with city and 38 other fieldsHigh correlation
state_Bolivar is highly overall correlated with city and 2 other fieldsHigh correlation
state_Chimborazo is highly overall correlated with city and 2 other fieldsHigh correlation
state_Cotopaxi is highly overall correlated with city and 3 other fieldsHigh correlation
state_El Oro is highly overall correlated with city and 3 other fieldsHigh correlation
state_Esmeraldas is highly overall correlated with city and 2 other fieldsHigh correlation
state_Guayas is highly overall correlated with city and 5 other fieldsHigh correlation
state_Imbabura is highly overall correlated with city and 2 other fieldsHigh correlation
state_Loja is highly overall correlated with city and 2 other fieldsHigh correlation
state_Los Rios is highly overall correlated with city and 3 other fieldsHigh correlation
state_Manabi is highly overall correlated with city and 4 other fieldsHigh correlation
state_Pastaza is highly overall correlated with city and 2 other fieldsHigh correlation
state_Pichincha is highly overall correlated with city and 5 other fieldsHigh correlation
state_Santa Elena is highly overall correlated with city and 2 other fieldsHigh correlation
state_Santo Domingo de los Tsachilas is highly overall correlated with city and 2 other fieldsHigh correlation
state_Tungurahua is highly overall correlated with city and 1 other fieldsHigh correlation
store_nbr is highly overall correlated with city and 15 other fieldsHigh correlation
type is highly overall correlated with city and 8 other fieldsHigh correlation
type_B is highly overall correlated with city and 3 other fieldsHigh correlation
type_C is highly overall correlated with city and 5 other fieldsHigh correlation
type_D is highly overall correlated with city and 4 other fieldsHigh correlation
type_E is highly overall correlated with city and 4 other fieldsHigh correlation
is_payday is highly imbalanced (65.1%) Imbalance
city_Babahoyo is highly imbalanced (87.1%) Imbalance
city_Cayambe is highly imbalanced (86.8%) Imbalance
city_Cuenca is highly imbalanced (69.0%) Imbalance
city_Daule is highly imbalanced (86.7%) Imbalance
city_El Carmen is highly imbalanced (86.8%) Imbalance
city_Esmeraldas is highly imbalanced (86.7%) Imbalance
city_Guaranda is highly imbalanced (86.7%) Imbalance
city_Ibarra is highly imbalanced (86.7%) Imbalance
city_Latacunga is highly imbalanced (77.0%) Imbalance
city_Libertad is highly imbalanced (86.6%) Imbalance
city_Loja is highly imbalanced (86.4%) Imbalance
city_Machala is highly imbalanced (77.4%) Imbalance
city_Manta is highly imbalanced (77.1%) Imbalance
city_Playas is highly imbalanced (86.6%) Imbalance
city_Puyo is highly imbalanced (86.7%) Imbalance
city_Quevedo is highly imbalanced (87.0%) Imbalance
city_Riobamba is highly imbalanced (86.8%) Imbalance
city_Salinas is highly imbalanced (86.6%) Imbalance
city_Santo Domingo is highly imbalanced (68.6%) Imbalance
state_Bolivar is highly imbalanced (86.7%) Imbalance
state_Chimborazo is highly imbalanced (86.8%) Imbalance
state_Cotopaxi is highly imbalanced (77.0%) Imbalance
state_El Oro is highly imbalanced (77.4%) Imbalance
state_Esmeraldas is highly imbalanced (86.7%) Imbalance
state_Imbabura is highly imbalanced (86.7%) Imbalance
state_Loja is highly imbalanced (86.4%) Imbalance
state_Los Rios is highly imbalanced (77.7%) Imbalance
state_Manabi is highly imbalanced (69.1%) Imbalance
state_Pastaza is highly imbalanced (86.7%) Imbalance
state_Santa Elena is highly imbalanced (86.6%) Imbalance
state_Santo Domingo de los Tsachilas is highly imbalanced (68.6%) Imbalance
state_Tungurahua is highly imbalanced (77.0%) Imbalance
type_E is highly imbalanced (61.9%) Imbalance
family_BABY CARE is highly imbalanced (80.4%) Imbalance
family_BEAUTY is highly imbalanced (80.6%) Imbalance
family_BEVERAGES is highly imbalanced (80.7%) Imbalance
family_BOOKS is highly imbalanced (80.4%) Imbalance
family_BREAD/BAKERY is highly imbalanced (80.4%) Imbalance
family_CELEBRATION is highly imbalanced (80.5%) Imbalance
family_CLEANING is highly imbalanced (80.2%) Imbalance
family_DAIRY is highly imbalanced (80.2%) Imbalance
family_DELI is highly imbalanced (80.5%) Imbalance
family_EGGS is highly imbalanced (80.2%) Imbalance
family_FROZEN FOODS is highly imbalanced (80.4%) Imbalance
family_GROCERY I is highly imbalanced (80.6%) Imbalance
family_GROCERY II is highly imbalanced (80.6%) Imbalance
family_HARDWARE is highly imbalanced (80.5%) Imbalance
family_HOME AND KITCHEN I is highly imbalanced (80.6%) Imbalance
family_HOME AND KITCHEN II is highly imbalanced (80.8%) Imbalance
family_HOME APPLIANCES is highly imbalanced (80.4%) Imbalance
family_HOME CARE is highly imbalanced (80.1%) Imbalance
family_LADIESWEAR is highly imbalanced (80.6%) Imbalance
family_LAWN AND GARDEN is highly imbalanced (80.2%) Imbalance
family_LINGERIE is highly imbalanced (80.4%) Imbalance
family_LIQUOR,WINE,BEER is highly imbalanced (80.8%) Imbalance
family_MAGAZINES is highly imbalanced (80.4%) Imbalance
family_MEATS is highly imbalanced (80.3%) Imbalance
family_PERSONAL CARE is highly imbalanced (80.4%) Imbalance
family_PET SUPPLIES is highly imbalanced (80.4%) Imbalance
family_PLAYERS AND ELECTRONICS is highly imbalanced (80.2%) Imbalance
family_POULTRY is highly imbalanced (80.3%) Imbalance
family_PREPARED FOODS is highly imbalanced (80.3%) Imbalance
family_PRODUCE is highly imbalanced (80.4%) Imbalance
family_SCHOOL AND OFFICE SUPPLIES is highly imbalanced (80.4%) Imbalance
family_SEAFOOD is highly imbalanced (80.4%) Imbalance
is_holiday is highly imbalanced (82.3%) Imbalance
is_event is highly imbalanced (79.1%) Imbalance
is_additional is highly imbalanced (86.6%) Imbalance
is_transfer is highly imbalanced (96.0%) Imbalance
is_bridge is highly imbalanced (98.0%) Imbalance
id has unique values Unique
sales has 93676 (31.2%) zeros Zeros
onpromotion has 238782 (79.6%) zeros Zeros
days_to_next_payday has 19637 (6.5%) zeros Zeros
days_from_previous_payday has 19637 (6.5%) zeros Zeros
days_after_earthquake has 212988 (71.0%) zeros Zeros

Reproduction

Analysis started2025-03-23 13:41:34.151806
Analysis finished2025-03-23 13:43:08.794708
Duration1 minute and 34.64 seconds
Software versionydata-profiling vv4.12.2
Download configurationconfig.json

Variables

id
Real number (ℝ)

High correlation  Unique 

Distinct300089
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1502654.1
Minimum28
Maximum3000885
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2025-03-23T15:43:08.873712image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum28
5-th percentile149767.8
Q1751211
median1502579
Q32254795
95-th percentile2853000.4
Maximum3000885
Range3000857
Interquartile range (IQR)1503584

Descriptive statistics

Standard deviation867048.09
Coefficient of variation (CV)0.57701109
Kurtosis-1.2014971
Mean1502654.1
Median Absolute Deviation (MAD)751763
Skewness-0.0024746191
Sum4.5092997 × 1011
Variance7.517724 × 1011
MonotonicityNot monotonic
2025-03-23T15:43:08.981848image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1056538 1
 
< 0.1%
298189 1
 
< 0.1%
122546 1
 
< 0.1%
1647768 1
 
< 0.1%
844276 1
 
< 0.1%
2178629 1
 
< 0.1%
947306 1
 
< 0.1%
2273742 1
 
< 0.1%
2566179 1
 
< 0.1%
2188553 1
 
< 0.1%
Other values (300079) 300079
> 99.9%
ValueCountFrequency (%)
28 1
< 0.1%
37 1
< 0.1%
42 1
< 0.1%
43 1
< 0.1%
58 1
< 0.1%
82 1
< 0.1%
84 1
< 0.1%
85 1
< 0.1%
98 1
< 0.1%
100 1
< 0.1%
ValueCountFrequency (%)
3000885 1
< 0.1%
3000874 1
< 0.1%
3000873 1
< 0.1%
3000869 1
< 0.1%
3000850 1
< 0.1%
3000838 1
< 0.1%
3000809 1
< 0.1%
3000797 1
< 0.1%
3000793 1
< 0.1%
3000781 1
< 0.1%

date
Date

Distinct1684
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
Minimum2013-01-01 00:00:00
Maximum2017-08-15 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-03-23T15:43:09.086319image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T15:43:09.197299image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

store_nbr
Real number (ℝ)

High correlation 

Distinct54
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.475256
Minimum1
Maximum54
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2025-03-23T15:43:09.305959image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q114
median27
Q341
95-th percentile52
Maximum54
Range53
Interquartile range (IQR)27

Descriptive statistics

Standard deviation15.569054
Coefficient of variation (CV)0.5666573
Kurtosis-1.1981555
Mean27.475256
Median Absolute Deviation (MAD)13
Skewness0.0024651484
Sum8245022
Variance242.39545
MonotonicityNot monotonic
2025-03-23T15:43:09.409838image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
38 5737
 
1.9%
21 5737
 
1.9%
20 5720
 
1.9%
37 5682
 
1.9%
1 5654
 
1.9%
5 5646
 
1.9%
23 5641
 
1.9%
35 5631
 
1.9%
12 5625
 
1.9%
36 5619
 
1.9%
Other values (44) 243397
81.1%
ValueCountFrequency (%)
1 5654
1.9%
2 5552
1.9%
3 5504
1.8%
4 5453
1.8%
5 5646
1.9%
6 5423
1.8%
7 5524
1.8%
8 5581
1.9%
9 5560
1.9%
10 5554
1.9%
ValueCountFrequency (%)
54 5493
1.8%
53 5523
1.8%
52 5607
1.9%
51 5434
1.8%
50 5594
1.9%
49 5487
1.8%
48 5525
1.8%
47 5596
1.9%
46 5576
1.9%
45 5565
1.9%

family
Categorical

High correlation 

Distinct33
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
HOME CARE
 
9258
PLAYERS AND ELECTRONICS
 
9238
LAWN AND GARDEN
 
9238
CLEANING
 
9214
EGGS
 
9214
Other values (28)
253927 

Length

Max length26
Median length16
Mean length10.750777
Min length4

Characters and Unicode

Total characters3226190
Distinct characters27
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBABY CARE
2nd rowHOME APPLIANCES
3rd rowGROCERY I
4th rowBOOKS
5th rowBEAUTY

Common Values

ValueCountFrequency (%)
HOME CARE 9258
 
3.1%
PLAYERS AND ELECTRONICS 9238
 
3.1%
LAWN AND GARDEN 9238
 
3.1%
CLEANING 9214
 
3.1%
EGGS 9214
 
3.1%
DAIRY 9213
 
3.1%
POULTRY 9189
 
3.1%
PREPARED FOODS 9185
 
3.1%
AUTOMOTIVE 9166
 
3.1%
MEATS 9136
 
3.0%
Other values (23) 208038
69.3%

Length

2025-03-23T15:43:09.510290image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
and 45456
 
9.1%
home 36248
 
7.3%
care 27496
 
5.5%
foods 18269
 
3.7%
supplies 18205
 
3.6%
i 18001
 
3.6%
grocery 17964
 
3.6%
kitchen 17872
 
3.6%
ii 17835
 
3.6%
players 9238
 
1.8%
Other values (30) 273341
54.7%

Most occurring characters

ValueCountFrequency (%)
E 435687
13.5%
A 291648
 
9.0%
R 245342
 
7.6%
O 236442
 
7.3%
I 216918
 
6.7%
199836
 
6.2%
N 172921
 
5.4%
S 164089
 
5.1%
D 145737
 
4.5%
L 136718
 
4.2%
Other values (17) 980852
30.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 2999508
93.0%
Space Separator 199836
 
6.2%
Other Punctuation 26846
 
0.8%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 435687
14.5%
A 291648
 
9.7%
R 245342
 
8.2%
O 236442
 
7.9%
I 216918
 
7.2%
N 172921
 
5.8%
S 164089
 
5.5%
D 145737
 
4.9%
L 136718
 
4.6%
C 136527
 
4.6%
Other values (14) 817479
27.3%
Other Punctuation
ValueCountFrequency (%)
, 17740
66.1%
/ 9106
33.9%
Space Separator
ValueCountFrequency (%)
199836
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2999508
93.0%
Common 226682
 
7.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 435687
14.5%
A 291648
 
9.7%
R 245342
 
8.2%
O 236442
 
7.9%
I 216918
 
7.2%
N 172921
 
5.8%
S 164089
 
5.5%
D 145737
 
4.9%
L 136718
 
4.6%
C 136527
 
4.6%
Other values (14) 817479
27.3%
Common
ValueCountFrequency (%)
199836
88.2%
, 17740
 
7.8%
/ 9106
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3226190
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 435687
13.5%
A 291648
 
9.0%
R 245342
 
7.6%
O 236442
 
7.3%
I 216918
 
6.7%
199836
 
6.2%
N 172921
 
5.4%
S 164089
 
5.1%
D 145737
 
4.5%
L 136718
 
4.2%
Other values (17) 980852
30.4%

sales
Real number (ℝ)

High correlation  Zeros 

Distinct51995
Distinct (%)17.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean357.96981
Minimum0
Maximum63434
Zeros93676
Zeros (%)31.2%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2025-03-23T15:43:09.600706image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median11
Q3196.576
95-th percentile1957.7828
Maximum63434
Range63434
Interquartile range (IQR)196.576

Descriptive statistics

Standard deviation1109.0809
Coefficient of variation (CV)3.0982526
Kurtosis109.22025
Mean357.96981
Median Absolute Deviation (MAD)11
Skewness7.2814034
Sum1.074228 × 108
Variance1230060.4
MonotonicityNot monotonic
2025-03-23T15:43:09.697925image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 93676
31.2%
1 11609
 
3.9%
2 8547
 
2.8%
3 6879
 
2.3%
4 5891
 
2.0%
5 5103
 
1.7%
6 4326
 
1.4%
7 3800
 
1.3%
8 3345
 
1.1%
9 3043
 
1.0%
Other values (51985) 153870
51.3%
ValueCountFrequency (%)
0 93676
31.2%
0.166 1
 
< 0.1%
0.224 1
 
< 0.1%
0.232 1
 
< 0.1%
0.318 1
 
< 0.1%
0.396 1
 
< 0.1%
0.582 1
 
< 0.1%
0.634 1
 
< 0.1%
0.788 1
 
< 0.1%
1 11609
 
3.9%
ValueCountFrequency (%)
63434 1
< 0.1%
46271 1
< 0.1%
37514.926 1
< 0.1%
34454 1
< 0.1%
33274 1
< 0.1%
29670 1
< 0.1%
29666 1
< 0.1%
22255 1
< 0.1%
21858 1
< 0.1%
21719 1
< 0.1%

onpromotion
Real number (ℝ)

High correlation  Zeros 

Distinct263
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.5939338
Minimum0
Maximum717
Zeros238782
Zeros (%)79.6%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2025-03-23T15:43:09.793595image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile13
Maximum717
Range717
Interquartile range (IQR)0

Descriptive statistics

Standard deviation12.268568
Coefficient of variation (CV)4.7297153
Kurtosis224.09479
Mean2.5939338
Median Absolute Deviation (MAD)0
Skewness11.155899
Sum778411
Variance150.51777
MonotonicityNot monotonic
2025-03-23T15:43:09.899143image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 238782
79.6%
1 17438
 
5.8%
2 8040
 
2.7%
3 4561
 
1.5%
4 3167
 
1.1%
5 2506
 
0.8%
6 2241
 
0.7%
7 1907
 
0.6%
8 1594
 
0.5%
9 1428
 
0.5%
Other values (253) 18425
 
6.1%
ValueCountFrequency (%)
0 238782
79.6%
1 17438
 
5.8%
2 8040
 
2.7%
3 4561
 
1.5%
4 3167
 
1.1%
5 2506
 
0.8%
6 2241
 
0.7%
7 1907
 
0.6%
8 1594
 
0.5%
9 1428
 
0.5%
ValueCountFrequency (%)
717 1
< 0.1%
626 1
< 0.1%
519 1
< 0.1%
496 1
< 0.1%
489 1
< 0.1%
485 1
< 0.1%
474 1
< 0.1%
446 1
< 0.1%
391 1
< 0.1%
332 1
< 0.1%

days_to_next_payday
Real number (ℝ)

High correlation  Zeros 

Distinct16
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.1273356
Minimum0
Maximum15
Zeros19637
Zeros (%)6.5%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2025-03-23T15:43:09.987665image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median7
Q311
95-th percentile14
Maximum15
Range15
Interquartile range (IQR)8

Descriptive statistics

Standard deviation4.4078243
Coefficient of variation (CV)0.61843928
Kurtosis-1.19466
Mean7.1273356
Median Absolute Deviation (MAD)4
Skewness0.012819369
Sum2138835
Variance19.428915
MonotonicityNot monotonic
2025-03-23T15:43:10.053744image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
11 19888
 
6.6%
3 19875
 
6.6%
5 19845
 
6.6%
1 19844
 
6.6%
10 19820
 
6.6%
12 19799
 
6.6%
7 19776
 
6.6%
9 19718
 
6.6%
4 19668
 
6.6%
2 19661
 
6.6%
Other values (6) 102195
34.1%
ValueCountFrequency (%)
0 19637
6.5%
1 19844
6.6%
2 19661
6.6%
3 19875
6.6%
4 19668
6.6%
5 19845
6.6%
6 19129
6.4%
7 19776
6.6%
8 19597
6.5%
9 19718
6.6%
ValueCountFrequency (%)
15 5660
 
1.9%
14 19011
6.3%
13 19161
6.4%
12 19799
6.6%
11 19888
6.6%
10 19820
6.6%
9 19718
6.6%
8 19597
6.5%
7 19776
6.6%
6 19129
6.4%

days_from_previous_payday
Real number (ℝ)

High correlation  Zeros 

Distinct16
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.1105006
Minimum0
Maximum15
Zeros19637
Zeros (%)6.5%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2025-03-23T15:43:10.119153image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median7
Q311
95-th percentile14
Maximum15
Range15
Interquartile range (IQR)8

Descriptive statistics

Standard deviation4.4059246
Coefficient of variation (CV)0.61963635
Kurtosis-1.1907205
Mean7.1105006
Median Absolute Deviation (MAD)4
Skewness0.021541351
Sum2133783
Variance19.412172
MonotonicityNot monotonic
2025-03-23T15:43:10.185579image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
1 19877
 
6.6%
2 19865
 
6.6%
4 19859
 
6.6%
9 19843
 
6.6%
5 19806
 
6.6%
6 19799
 
6.6%
8 19742
 
6.6%
11 19732
 
6.6%
3 19731
 
6.6%
0 19637
 
6.5%
Other values (6) 102198
34.1%
ValueCountFrequency (%)
0 19637
6.5%
1 19877
6.6%
2 19865
6.6%
3 19731
6.6%
4 19859
6.6%
5 19806
6.6%
6 19799
6.6%
7 19620
6.5%
8 19742
6.6%
9 19843
6.6%
ValueCountFrequency (%)
15 5740
 
1.9%
14 18924
6.3%
13 19140
6.4%
12 19606
6.5%
11 19732
6.6%
10 19168
6.4%
9 19843
6.6%
8 19742
6.6%
7 19620
6.5%
6 19799
6.6%

is_payday
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
0
280452 
1
 
19637

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters300089
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 280452
93.5%
1 19637
 
6.5%

Length

2025-03-23T15:43:10.261096image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-23T15:43:10.314614image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 280452
93.5%
1 19637
 
6.5%

Most occurring characters

ValueCountFrequency (%)
0 280452
93.5%
1 19637
 
6.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 300089
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 280452
93.5%
1 19637
 
6.5%

Most occurring scripts

ValueCountFrequency (%)
Common 300089
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 280452
93.5%
1 19637
 
6.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 300089
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 280452
93.5%
1 19637
 
6.5%

days_after_earthquake
Real number (ℝ)

High correlation  Zeros 

Distinct487
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean70.825215
Minimum0
Maximum487
Zeros212988
Zeros (%)71.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2025-03-23T15:43:10.381415image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q368
95-th percentile405
Maximum487
Range487
Interquartile range (IQR)68

Descriptive statistics

Standard deviation134.3183
Coefficient of variation (CV)1.8964757
Kurtosis1.6804453
Mean70.825215
Median Absolute Deviation (MAD)0
Skewness1.7559898
Sum21253868
Variance18041.406
MonotonicityNot monotonic
2025-03-23T15:43:10.478184image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 212988
71.0%
341 219
 
0.1%
260 215
 
0.1%
378 215
 
0.1%
35 214
 
0.1%
275 213
 
0.1%
462 212
 
0.1%
216 211
 
0.1%
154 211
 
0.1%
39 210
 
0.1%
Other values (477) 85181
 
28.4%
ValueCountFrequency (%)
0 212988
71.0%
1 181
 
0.1%
2 170
 
0.1%
3 183
 
0.1%
4 177
 
0.1%
5 174
 
0.1%
6 205
 
0.1%
7 185
 
0.1%
8 189
 
0.1%
9 176
 
0.1%
ValueCountFrequency (%)
487 189
0.1%
486 177
0.1%
485 195
0.1%
484 170
0.1%
483 190
0.1%
482 153
0.1%
481 181
0.1%
480 183
0.1%
479 166
0.1%
478 198
0.1%

dcoilwtico
Real number (ℝ)

High correlation 

Distinct994
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean67.879521
Minimum26.19
Maximum110.62
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2025-03-23T15:43:10.571860image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum26.19
5-th percentile36.81
Q146.32
median53.41
Q395.72
95-th percentile105.23
Maximum110.62
Range84.43
Interquartile range (IQR)49.4

Descriptive statistics

Standard deviation25.656601
Coefficient of variation (CV)0.37797264
Kurtosis-1.616566
Mean67.879521
Median Absolute Deviation (MAD)12.96
Skewness0.30848722
Sum20369898
Variance658.2612
MonotonicityNot monotonic
2025-03-23T15:43:10.665105image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
46.02 1998
 
0.7%
93.12 1430
 
0.5%
97.86 1380
 
0.5%
104.76 1226
 
0.4%
48.49 1100
 
0.4%
94.56 1090
 
0.4%
103.83 1076
 
0.4%
59.41 1062
 
0.4%
47.3 1027
 
0.3%
47.72 1010
 
0.3%
Other values (984) 287690
95.9%
ValueCountFrequency (%)
26.19 180
 
0.1%
26.68 185
 
0.1%
27.54 182
 
0.1%
27.96 151
 
0.1%
28.47 178
 
0.1%
29.05 194
 
0.1%
29.32 718
0.2%
29.45 720
0.2%
29.54 177
 
0.1%
29.55 170
 
0.1%
ValueCountFrequency (%)
110.62 569
0.2%
110.17 180
 
0.1%
109.62 177
 
0.1%
109.11 187
 
0.1%
108.72 169
 
0.1%
108.67 178
 
0.1%
108.51 192
 
0.1%
108.5 130
 
< 0.1%
108.31 538
0.2%
108.23 165
 
0.1%

city
Categorical

High correlation 

Distinct22
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
Quito
99924 
Guayaquil
44285 
Santo Domingo
16997 
Cuenca
16716 
Ambato
11235 
Other values (17)
110932 

Length

Max length13
Median length10
Mean length6.8545065
Min length4

Characters and Unicode

Total characters2056962
Distinct characters34
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGuayaquil
2nd rowQuito
3rd rowCuenca
4th rowQuito
5th rowLoja

Common Values

ValueCountFrequency (%)
Quito 99924
33.3%
Guayaquil 44285
14.8%
Santo Domingo 16997
 
5.7%
Cuenca 16716
 
5.6%
Ambato 11235
 
3.7%
Latacunga 11179
 
3.7%
Manta 11130
 
3.7%
Machala 10960
 
3.7%
Loja 5737
 
1.9%
Playas 5631
 
1.9%
Other values (12) 66295
22.1%

Length

2025-03-23T15:43:10.751601image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
quito 99924
31.0%
guayaquil 44285
13.7%
santo 16997
 
5.3%
domingo 16997
 
5.3%
cuenca 16716
 
5.2%
ambato 11235
 
3.5%
latacunga 11179
 
3.5%
manta 11130
 
3.5%
machala 10960
 
3.4%
loja 5737
 
1.8%
Other values (14) 77419
24.0%

Most occurring characters

ValueCountFrequency (%)
a 338708
16.5%
u 238491
11.6%
o 195103
 
9.5%
i 177927
 
8.6%
t 156084
 
7.6%
Q 105345
 
5.1%
n 89653
 
4.4%
l 83071
 
4.0%
y 66346
 
3.2%
e 55302
 
2.7%
Other values (24) 550932
26.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1711893
83.2%
Uppercase Letter 322579
 
15.7%
Space Separator 22490
 
1.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 338708
19.8%
u 238491
13.9%
o 195103
11.4%
i 177927
10.4%
t 156084
9.1%
n 89653
 
5.2%
l 83071
 
4.9%
y 66346
 
3.9%
e 55302
 
3.2%
m 50306
 
2.9%
Other values (10) 260902
15.2%
Uppercase Letter
ValueCountFrequency (%)
Q 105345
32.7%
G 49842
15.5%
C 27723
 
8.6%
S 22581
 
7.0%
D 22566
 
7.0%
L 22535
 
7.0%
M 22090
 
6.8%
A 11235
 
3.5%
P 11186
 
3.5%
E 11042
 
3.4%
Other values (3) 16434
 
5.1%
Space Separator
ValueCountFrequency (%)
22490
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2034472
98.9%
Common 22490
 
1.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 338708
16.6%
u 238491
11.7%
o 195103
 
9.6%
i 177927
 
8.7%
t 156084
 
7.7%
Q 105345
 
5.2%
n 89653
 
4.4%
l 83071
 
4.1%
y 66346
 
3.3%
e 55302
 
2.7%
Other values (23) 528442
26.0%
Common
ValueCountFrequency (%)
22490
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2056962
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 338708
16.5%
u 238491
11.6%
o 195103
 
9.5%
i 177927
 
8.6%
t 156084
 
7.6%
Q 105345
 
5.1%
n 89653
 
4.4%
l 83071
 
4.0%
y 66346
 
3.2%
e 55302
 
2.7%
Other values (24) 550932
26.8%

state
Categorical

High correlation 

Distinct16
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
Pichincha
105438 
Guayas
61104 
Santo Domingo de los Tsachilas
16997 
Azuay
16716 
Manabi
16623 
Other values (11)
83211 

Length

Max length30
Median length11
Mean length8.9302107
Min length4

Characters and Unicode

Total characters2679858
Distinct characters37
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGuayas
2nd rowPichincha
3rd rowAzuay
4th rowPichincha
5th rowLoja

Common Values

ValueCountFrequency (%)
Pichincha 105438
35.1%
Guayas 61104
20.4%
Santo Domingo de los Tsachilas 16997
 
5.7%
Azuay 16716
 
5.6%
Manabi 16623
 
5.5%
Tungurahua 11235
 
3.7%
Cotopaxi 11179
 
3.7%
El Oro 10960
 
3.7%
Los Rios 10782
 
3.6%
Loja 5737
 
1.9%
Other values (6) 33318
 
11.1%

Length

2025-03-23T15:43:10.833004image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
pichincha 105438
26.7%
guayas 61104
15.5%
los 27779
 
7.0%
santo 16997
 
4.3%
domingo 16997
 
4.3%
de 16997
 
4.3%
tsachilas 16997
 
4.3%
azuay 16716
 
4.2%
manabi 16623
 
4.2%
tungurahua 11235
 
2.8%
Other values (12) 88520
22.4%

Most occurring characters

ValueCountFrequency (%)
a 434685
16.2%
i 294529
11.0%
h 244626
 
9.1%
c 227873
 
8.5%
n 178458
 
6.7%
s 150312
 
5.6%
o 145200
 
5.4%
u 117080
 
4.4%
P 110993
 
4.1%
95314
 
3.6%
Other values (27) 680788
25.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2223135
83.0%
Uppercase Letter 361409
 
13.5%
Space Separator 95314
 
3.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 434685
19.6%
i 294529
13.2%
h 244626
11.0%
c 227873
10.3%
n 178458
8.0%
s 150312
 
6.8%
o 145200
 
6.5%
u 117080
 
5.3%
y 77820
 
3.5%
l 61644
 
2.8%
Other values (12) 290908
13.1%
Uppercase Letter
ValueCountFrequency (%)
P 110993
30.7%
G 61104
16.9%
T 28232
 
7.8%
S 22581
 
6.2%
E 22093
 
6.1%
D 16997
 
4.7%
A 16716
 
4.6%
C 16697
 
4.6%
M 16623
 
4.6%
L 16519
 
4.6%
Other values (4) 32854
 
9.1%
Space Separator
ValueCountFrequency (%)
95314
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2584544
96.4%
Common 95314
 
3.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 434685
16.8%
i 294529
11.4%
h 244626
9.5%
c 227873
 
8.8%
n 178458
 
6.9%
s 150312
 
5.8%
o 145200
 
5.6%
u 117080
 
4.5%
P 110993
 
4.3%
y 77820
 
3.0%
Other values (26) 602968
23.3%
Common
ValueCountFrequency (%)
95314
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2679858
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 434685
16.2%
i 294529
11.0%
h 244626
 
9.1%
c 227873
 
8.5%
n 178458
 
6.7%
s 150312
 
5.6%
o 145200
 
5.4%
u 117080
 
4.4%
P 110993
 
4.1%
95314
 
3.6%
Other values (27) 680788
25.4%

type
Categorical

High correlation 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
D
100239 
C
83146 
A
49873 
B
44597 
E
22234 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters300089
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowD
2nd rowA
3rd rowD
4th rowA
5th rowD

Common Values

ValueCountFrequency (%)
D 100239
33.4%
C 83146
27.7%
A 49873
16.6%
B 44597
14.9%
E 22234
 
7.4%

Length

2025-03-23T15:43:10.909051image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-23T15:43:10.966384image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
d 100239
33.4%
c 83146
27.7%
a 49873
16.6%
b 44597
14.9%
e 22234
 
7.4%

Most occurring characters

ValueCountFrequency (%)
D 100239
33.4%
C 83146
27.7%
A 49873
16.6%
B 44597
14.9%
E 22234
 
7.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 300089
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
D 100239
33.4%
C 83146
27.7%
A 49873
16.6%
B 44597
14.9%
E 22234
 
7.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 300089
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
D 100239
33.4%
C 83146
27.7%
A 49873
16.6%
B 44597
14.9%
E 22234
 
7.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 300089
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
D 100239
33.4%
C 83146
27.7%
A 49873
16.6%
B 44597
14.9%
E 22234
 
7.4%

cluster
Real number (ℝ)

High correlation 

Distinct17
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.4768452
Minimum1
Maximum17
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2025-03-23T15:43:11.028347image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median8
Q313
95-th percentile15
Maximum17
Range16
Interquartile range (IQR)9

Descriptive statistics

Standard deviation4.6492276
Coefficient of variation (CV)0.54846202
Kurtosis-1.2573134
Mean8.4768452
Median Absolute Deviation (MAD)4
Skewness0.041019303
Sum2543808
Variance21.615317
MonotonicityNot monotonic
2025-03-23T15:43:11.095321image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
3 38654
12.9%
6 33649
11.2%
10 33212
11.1%
15 27845
9.3%
14 22291
7.4%
13 22152
 
7.4%
4 16856
 
5.6%
1 16720
 
5.6%
11 16659
 
5.6%
8 16609
 
5.5%
Other values (7) 55442
18.5%
ValueCountFrequency (%)
1 16720
5.6%
2 11191
 
3.7%
3 38654
12.9%
4 16856
5.6%
5 5489
 
1.8%
6 33649
11.2%
7 11073
 
3.7%
8 16609
5.5%
9 11094
 
3.7%
10 33212
11.1%
ValueCountFrequency (%)
17 5434
 
1.8%
16 5587
 
1.9%
15 27845
9.3%
14 22291
7.4%
13 22152
7.4%
12 5574
 
1.9%
11 16659
5.6%
10 33212
11.1%
9 11094
 
3.7%
8 16609
5.5%

cluster.1
Real number (ℝ)

High correlation 

Distinct17
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.4768452
Minimum1
Maximum17
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2025-03-23T15:43:11.159646image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median8
Q313
95-th percentile15
Maximum17
Range16
Interquartile range (IQR)9

Descriptive statistics

Standard deviation4.6492276
Coefficient of variation (CV)0.54846202
Kurtosis-1.2573134
Mean8.4768452
Median Absolute Deviation (MAD)4
Skewness0.041019303
Sum2543808
Variance21.615317
MonotonicityNot monotonic
2025-03-23T15:43:11.226569image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
3 38654
12.9%
6 33649
11.2%
10 33212
11.1%
15 27845
9.3%
14 22291
7.4%
13 22152
 
7.4%
4 16856
 
5.6%
1 16720
 
5.6%
11 16659
 
5.6%
8 16609
 
5.5%
Other values (7) 55442
18.5%
ValueCountFrequency (%)
1 16720
5.6%
2 11191
 
3.7%
3 38654
12.9%
4 16856
5.6%
5 5489
 
1.8%
6 33649
11.2%
7 11073
 
3.7%
8 16609
5.5%
9 11094
 
3.7%
10 33212
11.1%
ValueCountFrequency (%)
17 5434
 
1.8%
16 5587
 
1.9%
15 27845
9.3%
14 22291
7.4%
13 22152
7.4%
12 5574
 
1.9%
11 16659
5.6%
10 33212
11.1%
9 11094
 
3.7%
8 16609
5.5%

city_Babahoyo
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
0
294728 
1
 
5361

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters300089
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 294728
98.2%
1 5361
 
1.8%

Length

2025-03-23T15:43:11.301912image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-23T15:43:11.347033image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 294728
98.2%
1 5361
 
1.8%

Most occurring characters

ValueCountFrequency (%)
0 294728
98.2%
1 5361
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 300089
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 294728
98.2%
1 5361
 
1.8%

Most occurring scripts

ValueCountFrequency (%)
Common 300089
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 294728
98.2%
1 5361
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 300089
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 294728
98.2%
1 5361
 
1.8%

city_Cayambe
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
0
294575 
1
 
5514

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters300089
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 294575
98.2%
1 5514
 
1.8%

Length

2025-03-23T15:43:11.402487image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-23T15:43:11.447484image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 294575
98.2%
1 5514
 
1.8%

Most occurring characters

ValueCountFrequency (%)
0 294575
98.2%
1 5514
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 300089
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 294575
98.2%
1 5514
 
1.8%

Most occurring scripts

ValueCountFrequency (%)
Common 300089
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 294575
98.2%
1 5514
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 300089
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 294575
98.2%
1 5514
 
1.8%

city_Cuenca
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
0
283373 
1
 
16716

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters300089
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 283373
94.4%
1 16716
 
5.6%

Length

2025-03-23T15:43:11.502670image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-23T15:43:11.547654image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 283373
94.4%
1 16716
 
5.6%

Most occurring characters

ValueCountFrequency (%)
0 283373
94.4%
1 16716
 
5.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 300089
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 283373
94.4%
1 16716
 
5.6%

Most occurring scripts

ValueCountFrequency (%)
Common 300089
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 283373
94.4%
1 16716
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 300089
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 283373
94.4%
1 16716
 
5.6%

city_Daule
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
0
294520 
1
 
5569

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters300089
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 294520
98.1%
1 5569
 
1.9%

Length

2025-03-23T15:43:11.602527image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-23T15:43:11.648177image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 294520
98.1%
1 5569
 
1.9%

Most occurring characters

ValueCountFrequency (%)
0 294520
98.1%
1 5569
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 300089
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 294520
98.1%
1 5569
 
1.9%

Most occurring scripts

ValueCountFrequency (%)
Common 300089
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 294520
98.1%
1 5569
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 300089
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 294520
98.1%
1 5569
 
1.9%

city_El Carmen
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
0
294596 
1
 
5493

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters300089
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 294596
98.2%
1 5493
 
1.8%

Length

2025-03-23T15:43:11.702878image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-23T15:43:11.748758image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 294596
98.2%
1 5493
 
1.8%

Most occurring characters

ValueCountFrequency (%)
0 294596
98.2%
1 5493
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 300089
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 294596
98.2%
1 5493
 
1.8%

Most occurring scripts

ValueCountFrequency (%)
Common 300089
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 294596
98.2%
1 5493
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 300089
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 294596
98.2%
1 5493
 
1.8%

city_Esmeraldas
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
0
294540 
1
 
5549

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters300089
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 294540
98.2%
1 5549
 
1.8%

Length

2025-03-23T15:43:11.804042image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-23T15:43:11.848963image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 294540
98.2%
1 5549
 
1.8%

Most occurring characters

ValueCountFrequency (%)
0 294540
98.2%
1 5549
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 300089
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 294540
98.2%
1 5549
 
1.8%

Most occurring scripts

ValueCountFrequency (%)
Common 300089
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 294540
98.2%
1 5549
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 300089
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 294540
98.2%
1 5549
 
1.8%

city_Guaranda
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
0
294532 
1
 
5557

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters300089
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 294532
98.1%
1 5557
 
1.9%

Length

2025-03-23T15:43:11.903742image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-23T15:43:11.949250image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 294532
98.1%
1 5557
 
1.9%

Most occurring characters

ValueCountFrequency (%)
0 294532
98.1%
1 5557
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 300089
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 294532
98.1%
1 5557
 
1.9%

Most occurring scripts

ValueCountFrequency (%)
Common 300089
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 294532
98.1%
1 5557
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 300089
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 294532
98.1%
1 5557
 
1.9%

city_Guayaquil
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
0
255804 
1
44285 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters300089
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 255804
85.2%
1 44285
 
14.8%

Length

2025-03-23T15:43:12.003808image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-23T15:43:12.049543image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 255804
85.2%
1 44285
 
14.8%

Most occurring characters

ValueCountFrequency (%)
0 255804
85.2%
1 44285
 
14.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 300089
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 255804
85.2%
1 44285
 
14.8%

Most occurring scripts

ValueCountFrequency (%)
Common 300089
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 255804
85.2%
1 44285
 
14.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 300089
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 255804
85.2%
1 44285
 
14.8%

city_Ibarra
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
0
294534 
1
 
5555

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters300089
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 294534
98.1%
1 5555
 
1.9%

Length

2025-03-23T15:43:12.309784image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-23T15:43:12.354819image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 294534
98.1%
1 5555
 
1.9%

Most occurring characters

ValueCountFrequency (%)
0 294534
98.1%
1 5555
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 300089
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 294534
98.1%
1 5555
 
1.9%

Most occurring scripts

ValueCountFrequency (%)
Common 300089
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 294534
98.1%
1 5555
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 300089
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 294534
98.1%
1 5555
 
1.9%

city_Latacunga
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
0
288910 
1
 
11179

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters300089
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 288910
96.3%
1 11179
 
3.7%

Length

2025-03-23T15:43:12.410429image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-23T15:43:12.455356image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 288910
96.3%
1 11179
 
3.7%

Most occurring characters

ValueCountFrequency (%)
0 288910
96.3%
1 11179
 
3.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 300089
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 288910
96.3%
1 11179
 
3.7%

Most occurring scripts

ValueCountFrequency (%)
Common 300089
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 288910
96.3%
1 11179
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 300089
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 288910
96.3%
1 11179
 
3.7%

city_Libertad
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
0
294470 
1
 
5619

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters300089
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 294470
98.1%
1 5619
 
1.9%

Length

2025-03-23T15:43:12.509879image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-23T15:43:12.555504image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 294470
98.1%
1 5619
 
1.9%

Most occurring characters

ValueCountFrequency (%)
0 294470
98.1%
1 5619
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 300089
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 294470
98.1%
1 5619
 
1.9%

Most occurring scripts

ValueCountFrequency (%)
Common 300089
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 294470
98.1%
1 5619
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 300089
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 294470
98.1%
1 5619
 
1.9%

city_Loja
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
0
294352 
1
 
5737

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters300089
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 294352
98.1%
1 5737
 
1.9%

Length

2025-03-23T15:43:12.610818image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-23T15:43:12.655639image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 294352
98.1%
1 5737
 
1.9%

Most occurring characters

ValueCountFrequency (%)
0 294352
98.1%
1 5737
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 300089
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 294352
98.1%
1 5737
 
1.9%

Most occurring scripts

ValueCountFrequency (%)
Common 300089
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 294352
98.1%
1 5737
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 300089
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 294352
98.1%
1 5737
 
1.9%

city_Machala
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
0
289129 
1
 
10960

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters300089
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 289129
96.3%
1 10960
 
3.7%

Length

2025-03-23T15:43:12.710935image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-23T15:43:12.755802image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 289129
96.3%
1 10960
 
3.7%

Most occurring characters

ValueCountFrequency (%)
0 289129
96.3%
1 10960
 
3.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 300089
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 289129
96.3%
1 10960
 
3.7%

Most occurring scripts

ValueCountFrequency (%)
Common 300089
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 289129
96.3%
1 10960
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 300089
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 289129
96.3%
1 10960
 
3.7%

city_Manta
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
0
288959 
1
 
11130

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters300089
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 288959
96.3%
1 11130
 
3.7%

Length

2025-03-23T15:43:12.811314image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-23T15:43:12.856363image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 288959
96.3%
1 11130
 
3.7%

Most occurring characters

ValueCountFrequency (%)
0 288959
96.3%
1 11130
 
3.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 300089
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 288959
96.3%
1 11130
 
3.7%

Most occurring scripts

ValueCountFrequency (%)
Common 300089
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 288959
96.3%
1 11130
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 300089
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 288959
96.3%
1 11130
 
3.7%

city_Playas
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
0
294458 
1
 
5631

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters300089
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 294458
98.1%
1 5631
 
1.9%

Length

2025-03-23T15:43:12.910866image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-23T15:43:12.956420image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 294458
98.1%
1 5631
 
1.9%

Most occurring characters

ValueCountFrequency (%)
0 294458
98.1%
1 5631
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 300089
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 294458
98.1%
1 5631
 
1.9%

Most occurring scripts

ValueCountFrequency (%)
Common 300089
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 294458
98.1%
1 5631
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 300089
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 294458
98.1%
1 5631
 
1.9%

city_Puyo
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
0
294534 
1
 
5555

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters300089
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 294534
98.1%
1 5555
 
1.9%

Length

2025-03-23T15:43:13.011035image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-23T15:43:13.055824image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 294534
98.1%
1 5555
 
1.9%

Most occurring characters

ValueCountFrequency (%)
0 294534
98.1%
1 5555
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 300089
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 294534
98.1%
1 5555
 
1.9%

Most occurring scripts

ValueCountFrequency (%)
Common 300089
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 294534
98.1%
1 5555
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 300089
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 294534
98.1%
1 5555
 
1.9%

city_Quevedo
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
0
294668 
1
 
5421

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters300089
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 294668
98.2%
1 5421
 
1.8%

Length

2025-03-23T15:43:13.111104image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-23T15:43:13.155854image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 294668
98.2%
1 5421
 
1.8%

Most occurring characters

ValueCountFrequency (%)
0 294668
98.2%
1 5421
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 300089
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 294668
98.2%
1 5421
 
1.8%

Most occurring scripts

ValueCountFrequency (%)
Common 300089
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 294668
98.2%
1 5421
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 300089
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 294668
98.2%
1 5421
 
1.8%

city_Quito
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
0
200165 
1
99924 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters300089
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 200165
66.7%
1 99924
33.3%

Length

2025-03-23T15:43:13.211057image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-23T15:43:13.256636image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 200165
66.7%
1 99924
33.3%

Most occurring characters

ValueCountFrequency (%)
0 200165
66.7%
1 99924
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 300089
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 200165
66.7%
1 99924
33.3%

Most occurring scripts

ValueCountFrequency (%)
Common 300089
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 200165
66.7%
1 99924
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 300089
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 200165
66.7%
1 99924
33.3%

city_Riobamba
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
0
294571 
1
 
5518

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters300089
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 294571
98.2%
1 5518
 
1.8%

Length

2025-03-23T15:43:13.314101image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-23T15:43:13.359982image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 294571
98.2%
1 5518
 
1.8%

Most occurring characters

ValueCountFrequency (%)
0 294571
98.2%
1 5518
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 300089
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 294571
98.2%
1 5518
 
1.8%

Most occurring scripts

ValueCountFrequency (%)
Common 300089
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 294571
98.2%
1 5518
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 300089
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 294571
98.2%
1 5518
 
1.8%

city_Salinas
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
0
294505 
1
 
5584

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters300089
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 294505
98.1%
1 5584
 
1.9%

Length

2025-03-23T15:43:13.414379image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-23T15:43:13.459154image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 294505
98.1%
1 5584
 
1.9%

Most occurring characters

ValueCountFrequency (%)
0 294505
98.1%
1 5584
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 300089
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 294505
98.1%
1 5584
 
1.9%

Most occurring scripts

ValueCountFrequency (%)
Common 300089
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 294505
98.1%
1 5584
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 300089
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 294505
98.1%
1 5584
 
1.9%

city_Santo Domingo
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
0
283092 
1
 
16997

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters300089
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 283092
94.3%
1 16997
 
5.7%

Length

2025-03-23T15:43:13.514448image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-23T15:43:13.559244image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 283092
94.3%
1 16997
 
5.7%

Most occurring characters

ValueCountFrequency (%)
0 283092
94.3%
1 16997
 
5.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 300089
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 283092
94.3%
1 16997
 
5.7%

Most occurring scripts

ValueCountFrequency (%)
Common 300089
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 283092
94.3%
1 16997
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 300089
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 283092
94.3%
1 16997
 
5.7%

state_Bolivar
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
0
294532 
1
 
5557

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters300089
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 294532
98.1%
1 5557
 
1.9%

Length

2025-03-23T15:43:13.614738image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-23T15:43:13.659750image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 294532
98.1%
1 5557
 
1.9%

Most occurring characters

ValueCountFrequency (%)
0 294532
98.1%
1 5557
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 300089
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 294532
98.1%
1 5557
 
1.9%

Most occurring scripts

ValueCountFrequency (%)
Common 300089
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 294532
98.1%
1 5557
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 300089
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 294532
98.1%
1 5557
 
1.9%

state_Chimborazo
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
0
294571 
1
 
5518

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters300089
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 294571
98.2%
1 5518
 
1.8%

Length

2025-03-23T15:43:13.714532image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-23T15:43:13.760318image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 294571
98.2%
1 5518
 
1.8%

Most occurring characters

ValueCountFrequency (%)
0 294571
98.2%
1 5518
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 300089
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 294571
98.2%
1 5518
 
1.8%

Most occurring scripts

ValueCountFrequency (%)
Common 300089
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 294571
98.2%
1 5518
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 300089
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 294571
98.2%
1 5518
 
1.8%

state_Cotopaxi
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
0
288910 
1
 
11179

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters300089
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 288910
96.3%
1 11179
 
3.7%

Length

2025-03-23T15:43:13.815371image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-23T15:43:13.860110image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 288910
96.3%
1 11179
 
3.7%

Most occurring characters

ValueCountFrequency (%)
0 288910
96.3%
1 11179
 
3.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 300089
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 288910
96.3%
1 11179
 
3.7%

Most occurring scripts

ValueCountFrequency (%)
Common 300089
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 288910
96.3%
1 11179
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 300089
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 288910
96.3%
1 11179
 
3.7%

state_El Oro
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
0
289129 
1
 
10960

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters300089
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 289129
96.3%
1 10960
 
3.7%

Length

2025-03-23T15:43:13.915814image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-23T15:43:13.960828image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 289129
96.3%
1 10960
 
3.7%

Most occurring characters

ValueCountFrequency (%)
0 289129
96.3%
1 10960
 
3.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 300089
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 289129
96.3%
1 10960
 
3.7%

Most occurring scripts

ValueCountFrequency (%)
Common 300089
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 289129
96.3%
1 10960
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 300089
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 289129
96.3%
1 10960
 
3.7%

state_Esmeraldas
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
0
294540 
1
 
5549

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters300089
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 294540
98.2%
1 5549
 
1.8%

Length

2025-03-23T15:43:14.015589image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-23T15:43:14.061072image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 294540
98.2%
1 5549
 
1.8%

Most occurring characters

ValueCountFrequency (%)
0 294540
98.2%
1 5549
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 300089
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 294540
98.2%
1 5549
 
1.8%

Most occurring scripts

ValueCountFrequency (%)
Common 300089
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 294540
98.2%
1 5549
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 300089
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 294540
98.2%
1 5549
 
1.8%

state_Guayas
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
0
238985 
1
61104 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters300089
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 238985
79.6%
1 61104
 
20.4%

Length

2025-03-23T15:43:14.115828image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-23T15:43:14.161826image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 238985
79.6%
1 61104
 
20.4%

Most occurring characters

ValueCountFrequency (%)
0 238985
79.6%
1 61104
 
20.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 300089
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 238985
79.6%
1 61104
 
20.4%

Most occurring scripts

ValueCountFrequency (%)
Common 300089
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 238985
79.6%
1 61104
 
20.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 300089
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 238985
79.6%
1 61104
 
20.4%

state_Imbabura
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
0
294534 
1
 
5555

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters300089
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 294534
98.1%
1 5555
 
1.9%

Length

2025-03-23T15:43:14.219838image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-23T15:43:14.264594image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 294534
98.1%
1 5555
 
1.9%

Most occurring characters

ValueCountFrequency (%)
0 294534
98.1%
1 5555
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 300089
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 294534
98.1%
1 5555
 
1.9%

Most occurring scripts

ValueCountFrequency (%)
Common 300089
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 294534
98.1%
1 5555
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 300089
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 294534
98.1%
1 5555
 
1.9%

state_Loja
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
0
294352 
1
 
5737

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters300089
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 294352
98.1%
1 5737
 
1.9%

Length

2025-03-23T15:43:14.319969image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-23T15:43:14.364852image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 294352
98.1%
1 5737
 
1.9%

Most occurring characters

ValueCountFrequency (%)
0 294352
98.1%
1 5737
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 300089
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 294352
98.1%
1 5737
 
1.9%

Most occurring scripts

ValueCountFrequency (%)
Common 300089
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 294352
98.1%
1 5737
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 300089
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 294352
98.1%
1 5737
 
1.9%

state_Los Rios
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
0
289307 
1
 
10782

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters300089
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 289307
96.4%
1 10782
 
3.6%

Length

2025-03-23T15:43:14.419750image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-23T15:43:14.465410image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 289307
96.4%
1 10782
 
3.6%

Most occurring characters

ValueCountFrequency (%)
0 289307
96.4%
1 10782
 
3.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 300089
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 289307
96.4%
1 10782
 
3.6%

Most occurring scripts

ValueCountFrequency (%)
Common 300089
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 289307
96.4%
1 10782
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 300089
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 289307
96.4%
1 10782
 
3.6%

state_Manabi
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
0
283466 
1
 
16623

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters300089
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 283466
94.5%
1 16623
 
5.5%

Length

2025-03-23T15:43:14.520090image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-23T15:43:14.565150image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 283466
94.5%
1 16623
 
5.5%

Most occurring characters

ValueCountFrequency (%)
0 283466
94.5%
1 16623
 
5.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 300089
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 283466
94.5%
1 16623
 
5.5%

Most occurring scripts

ValueCountFrequency (%)
Common 300089
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 283466
94.5%
1 16623
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 300089
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 283466
94.5%
1 16623
 
5.5%

state_Pastaza
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
0
294534 
1
 
5555

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters300089
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 294534
98.1%
1 5555
 
1.9%

Length

2025-03-23T15:43:14.620493image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-23T15:43:14.665340image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 294534
98.1%
1 5555
 
1.9%

Most occurring characters

ValueCountFrequency (%)
0 294534
98.1%
1 5555
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 300089
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 294534
98.1%
1 5555
 
1.9%

Most occurring scripts

ValueCountFrequency (%)
Common 300089
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 294534
98.1%
1 5555
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 300089
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 294534
98.1%
1 5555
 
1.9%

state_Pichincha
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
0
194651 
1
105438 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters300089
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 194651
64.9%
1 105438
35.1%

Length

2025-03-23T15:43:14.720718image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-23T15:43:14.766560image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 194651
64.9%
1 105438
35.1%

Most occurring characters

ValueCountFrequency (%)
0 194651
64.9%
1 105438
35.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 300089
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 194651
64.9%
1 105438
35.1%

Most occurring scripts

ValueCountFrequency (%)
Common 300089
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 194651
64.9%
1 105438
35.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 300089
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 194651
64.9%
1 105438
35.1%

state_Santa Elena
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
0
294505 
1
 
5584

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters300089
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 294505
98.1%
1 5584
 
1.9%

Length

2025-03-23T15:43:14.824023image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-23T15:43:14.870378image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 294505
98.1%
1 5584
 
1.9%

Most occurring characters

ValueCountFrequency (%)
0 294505
98.1%
1 5584
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 300089
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 294505
98.1%
1 5584
 
1.9%

Most occurring scripts

ValueCountFrequency (%)
Common 300089
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 294505
98.1%
1 5584
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 300089
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 294505
98.1%
1 5584
 
1.9%

state_Santo Domingo de los Tsachilas
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
0
283092 
1
 
16997

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters300089
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 283092
94.3%
1 16997
 
5.7%

Length

2025-03-23T15:43:14.925129image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-23T15:43:14.970218image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 283092
94.3%
1 16997
 
5.7%

Most occurring characters

ValueCountFrequency (%)
0 283092
94.3%
1 16997
 
5.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 300089
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 283092
94.3%
1 16997
 
5.7%

Most occurring scripts

ValueCountFrequency (%)
Common 300089
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 283092
94.3%
1 16997
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 300089
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 283092
94.3%
1 16997
 
5.7%

state_Tungurahua
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
0
288854 
1
 
11235

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters300089
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 288854
96.3%
1 11235
 
3.7%

Length

2025-03-23T15:43:15.025692image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-23T15:43:15.070710image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 288854
96.3%
1 11235
 
3.7%

Most occurring characters

ValueCountFrequency (%)
0 288854
96.3%
1 11235
 
3.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 300089
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 288854
96.3%
1 11235
 
3.7%

Most occurring scripts

ValueCountFrequency (%)
Common 300089
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 288854
96.3%
1 11235
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 300089
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 288854
96.3%
1 11235
 
3.7%

type_B
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
0
255492 
1
44597 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters300089
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 255492
85.1%
1 44597
 
14.9%

Length

2025-03-23T15:43:15.125218image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-23T15:43:15.171796image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 255492
85.1%
1 44597
 
14.9%

Most occurring characters

ValueCountFrequency (%)
0 255492
85.1%
1 44597
 
14.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 300089
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 255492
85.1%
1 44597
 
14.9%

Most occurring scripts

ValueCountFrequency (%)
Common 300089
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 255492
85.1%
1 44597
 
14.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 300089
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 255492
85.1%
1 44597
 
14.9%

type_C
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
0
216943 
1
83146 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters300089
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 216943
72.3%
1 83146
 
27.7%

Length

2025-03-23T15:43:15.228587image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-23T15:43:15.274512image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 216943
72.3%
1 83146
 
27.7%

Most occurring characters

ValueCountFrequency (%)
0 216943
72.3%
1 83146
 
27.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 300089
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 216943
72.3%
1 83146
 
27.7%

Most occurring scripts

ValueCountFrequency (%)
Common 300089
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 216943
72.3%
1 83146
 
27.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 300089
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 216943
72.3%
1 83146
 
27.7%

type_D
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
0
199850 
1
100239 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters300089
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 199850
66.6%
1 100239
33.4%

Length

2025-03-23T15:43:15.332265image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-23T15:43:15.378940image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 199850
66.6%
1 100239
33.4%

Most occurring characters

ValueCountFrequency (%)
0 199850
66.6%
1 100239
33.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 300089
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 199850
66.6%
1 100239
33.4%

Most occurring scripts

ValueCountFrequency (%)
Common 300089
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 199850
66.6%
1 100239
33.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 300089
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 199850
66.6%
1 100239
33.4%

type_E
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
0
277855 
1
 
22234

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters300089
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 277855
92.6%
1 22234
 
7.4%

Length

2025-03-23T15:43:15.437802image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-23T15:43:15.483187image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 277855
92.6%
1 22234
 
7.4%

Most occurring characters

ValueCountFrequency (%)
0 277855
92.6%
1 22234
 
7.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 300089
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 277855
92.6%
1 22234
 
7.4%

Most occurring scripts

ValueCountFrequency (%)
Common 300089
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 277855
92.6%
1 22234
 
7.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 300089
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 277855
92.6%
1 22234
 
7.4%

family_BABY CARE
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
0
290975 
1
 
9114

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters300089
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 290975
97.0%
1 9114
 
3.0%

Length

2025-03-23T15:43:15.539001image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-23T15:43:15.585799image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 290975
97.0%
1 9114
 
3.0%

Most occurring characters

ValueCountFrequency (%)
0 290975
97.0%
1 9114
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 300089
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 290975
97.0%
1 9114
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
Common 300089
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 290975
97.0%
1 9114
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 300089
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 290975
97.0%
1 9114
 
3.0%

family_BEAUTY
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
0
291086 
1
 
9003

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters300089
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 291086
97.0%
1 9003
 
3.0%

Length

2025-03-23T15:43:15.642139image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-23T15:43:15.687530image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 291086
97.0%
1 9003
 
3.0%

Most occurring characters

ValueCountFrequency (%)
0 291086
97.0%
1 9003
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 300089
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 291086
97.0%
1 9003
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
Common 300089
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 291086
97.0%
1 9003
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 300089
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 291086
97.0%
1 9003
 
3.0%

family_BEVERAGES
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
0
291141 
1
 
8948

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters300089
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 291141
97.0%
1 8948
 
3.0%

Length

2025-03-23T15:43:15.743747image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-23T15:43:15.789507image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 291141
97.0%
1 8948
 
3.0%

Most occurring characters

ValueCountFrequency (%)
0 291141
97.0%
1 8948
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 300089
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 291141
97.0%
1 8948
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
Common 300089
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 291141
97.0%
1 8948
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 300089
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 291141
97.0%
1 8948
 
3.0%

family_BOOKS
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
0
290984 
1
 
9105

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters300089
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 290984
97.0%
1 9105
 
3.0%

Length

2025-03-23T15:43:15.845431image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-23T15:43:15.891584image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 290984
97.0%
1 9105
 
3.0%

Most occurring characters

ValueCountFrequency (%)
0 290984
97.0%
1 9105
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 300089
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 290984
97.0%
1 9105
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
Common 300089
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 290984
97.0%
1 9105
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 300089
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 290984
97.0%
1 9105
 
3.0%

family_BREAD/BAKERY
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
0
290983 
1
 
9106

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters300089
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 290983
97.0%
1 9106
 
3.0%

Length

2025-03-23T15:43:15.948714image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-23T15:43:15.994455image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 290983
97.0%
1 9106
 
3.0%

Most occurring characters

ValueCountFrequency (%)
0 290983
97.0%
1 9106
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 300089
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 290983
97.0%
1 9106
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
Common 300089
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 290983
97.0%
1 9106
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 300089
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 290983
97.0%
1 9106
 
3.0%

family_CELEBRATION
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
0
291031 
1
 
9058

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters300089
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 291031
97.0%
1 9058
 
3.0%

Length

2025-03-23T15:43:16.051026image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-23T15:43:16.097823image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 291031
97.0%
1 9058
 
3.0%

Most occurring characters

ValueCountFrequency (%)
0 291031
97.0%
1 9058
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 300089
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 291031
97.0%
1 9058
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
Common 300089
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 291031
97.0%
1 9058
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 300089
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 291031
97.0%
1 9058
 
3.0%

family_CLEANING
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
0
290875 
1
 
9214

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters300089
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 290875
96.9%
1 9214
 
3.1%

Length

2025-03-23T15:43:16.153256image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-23T15:43:16.198455image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 290875
96.9%
1 9214
 
3.1%

Most occurring characters

ValueCountFrequency (%)
0 290875
96.9%
1 9214
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 300089
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 290875
96.9%
1 9214
 
3.1%

Most occurring scripts

ValueCountFrequency (%)
Common 300089
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 290875
96.9%
1 9214
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 300089
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 290875
96.9%
1 9214
 
3.1%

family_DAIRY
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
0
290876 
1
 
9213

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters300089
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 290876
96.9%
1 9213
 
3.1%

Length

2025-03-23T15:43:16.252935image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-23T15:43:16.298633image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 290876
96.9%
1 9213
 
3.1%

Most occurring characters

ValueCountFrequency (%)
0 290876
96.9%
1 9213
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 300089
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 290876
96.9%
1 9213
 
3.1%

Most occurring scripts

ValueCountFrequency (%)
Common 300089
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 290876
96.9%
1 9213
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 300089
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 290876
96.9%
1 9213
 
3.1%

family_DELI
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
0
291038 
1
 
9051

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters300089
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 291038
97.0%
1 9051
 
3.0%

Length

2025-03-23T15:43:16.353766image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-23T15:43:16.399251image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 291038
97.0%
1 9051
 
3.0%

Most occurring characters

ValueCountFrequency (%)
0 291038
97.0%
1 9051
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 300089
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 291038
97.0%
1 9051
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
Common 300089
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 291038
97.0%
1 9051
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 300089
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 291038
97.0%
1 9051
 
3.0%

family_EGGS
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
0
290875 
1
 
9214

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters300089
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 290875
96.9%
1 9214
 
3.1%

Length

2025-03-23T15:43:16.455673image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-23T15:43:16.502746image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 290875
96.9%
1 9214
 
3.1%

Most occurring characters

ValueCountFrequency (%)
0 290875
96.9%
1 9214
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 300089
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 290875
96.9%
1 9214
 
3.1%

Most occurring scripts

ValueCountFrequency (%)
Common 300089
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 290875
96.9%
1 9214
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 300089
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 290875
96.9%
1 9214
 
3.1%

family_FROZEN FOODS
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
0
291005 
1
 
9084

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters300089
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 291005
97.0%
1 9084
 
3.0%

Length

2025-03-23T15:43:16.559822image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-23T15:43:16.605759image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 291005
97.0%
1 9084
 
3.0%

Most occurring characters

ValueCountFrequency (%)
0 291005
97.0%
1 9084
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 300089
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 291005
97.0%
1 9084
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
Common 300089
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 291005
97.0%
1 9084
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 300089
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 291005
97.0%
1 9084
 
3.0%

family_GROCERY I
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
0
291097 
1
 
8992

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters300089
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 291097
97.0%
1 8992
 
3.0%

Length

2025-03-23T15:43:16.660150image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-23T15:43:16.706292image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 291097
97.0%
1 8992
 
3.0%

Most occurring characters

ValueCountFrequency (%)
0 291097
97.0%
1 8992
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 300089
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 291097
97.0%
1 8992
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
Common 300089
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 291097
97.0%
1 8992
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 300089
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 291097
97.0%
1 8992
 
3.0%

family_GROCERY II
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
0
291117 
1
 
8972

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters300089
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 291117
97.0%
1 8972
 
3.0%

Length

2025-03-23T15:43:16.760622image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-23T15:43:16.805800image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 291117
97.0%
1 8972
 
3.0%

Most occurring characters

ValueCountFrequency (%)
0 291117
97.0%
1 8972
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 300089
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 291117
97.0%
1 8972
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
Common 300089
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 291117
97.0%
1 8972
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 300089
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 291117
97.0%
1 8972
 
3.0%

family_HARDWARE
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
0
291046 
1
 
9043

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters300089
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 291046
97.0%
1 9043
 
3.0%

Length

2025-03-23T15:43:16.861094image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-23T15:43:16.906229image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 291046
97.0%
1 9043
 
3.0%

Most occurring characters

ValueCountFrequency (%)
0 291046
97.0%
1 9043
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 300089
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 291046
97.0%
1 9043
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
Common 300089
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 291046
97.0%
1 9043
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 300089
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 291046
97.0%
1 9043
 
3.0%

family_HOME AND KITCHEN I
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
0
291080 
1
 
9009

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters300089
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 291080
97.0%
1 9009
 
3.0%

Length

2025-03-23T15:43:16.960656image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-23T15:43:17.006844image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 291080
97.0%
1 9009
 
3.0%

Most occurring characters

ValueCountFrequency (%)
0 291080
97.0%
1 9009
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 300089
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 291080
97.0%
1 9009
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
Common 300089
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 291080
97.0%
1 9009
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 300089
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 291080
97.0%
1 9009
 
3.0%

family_HOME AND KITCHEN II
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
0
291226 
1
 
8863

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters300089
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 291226
97.0%
1 8863
 
3.0%

Length

2025-03-23T15:43:17.061291image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-23T15:43:17.106325image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 291226
97.0%
1 8863
 
3.0%

Most occurring characters

ValueCountFrequency (%)
0 291226
97.0%
1 8863
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 300089
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 291226
97.0%
1 8863
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
Common 300089
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 291226
97.0%
1 8863
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 300089
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 291226
97.0%
1 8863
 
3.0%

family_HOME APPLIANCES
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
0
290971 
1
 
9118

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters300089
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 290971
97.0%
1 9118
 
3.0%

Length

2025-03-23T15:43:17.413855image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-23T15:43:17.459784image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 290971
97.0%
1 9118
 
3.0%

Most occurring characters

ValueCountFrequency (%)
0 290971
97.0%
1 9118
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 300089
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 290971
97.0%
1 9118
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
Common 300089
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 290971
97.0%
1 9118
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 300089
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 290971
97.0%
1 9118
 
3.0%

family_HOME CARE
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
0
290831 
1
 
9258

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters300089
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 290831
96.9%
1 9258
 
3.1%

Length

2025-03-23T15:43:17.515961image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-23T15:43:17.560888image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 290831
96.9%
1 9258
 
3.1%

Most occurring characters

ValueCountFrequency (%)
0 290831
96.9%
1 9258
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 300089
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 290831
96.9%
1 9258
 
3.1%

Most occurring scripts

ValueCountFrequency (%)
Common 300089
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 290831
96.9%
1 9258
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 300089
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 290831
96.9%
1 9258
 
3.1%

family_LADIESWEAR
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
0
291101 
1
 
8988

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters300089
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 291101
97.0%
1 8988
 
3.0%

Length

2025-03-23T15:43:17.616606image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-23T15:43:17.662201image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 291101
97.0%
1 8988
 
3.0%

Most occurring characters

ValueCountFrequency (%)
0 291101
97.0%
1 8988
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 300089
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 291101
97.0%
1 8988
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
Common 300089
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 291101
97.0%
1 8988
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 300089
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 291101
97.0%
1 8988
 
3.0%

family_LAWN AND GARDEN
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
0
290851 
1
 
9238

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters300089
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 290851
96.9%
1 9238
 
3.1%

Length

2025-03-23T15:43:17.716921image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-23T15:43:17.761843image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 290851
96.9%
1 9238
 
3.1%

Most occurring characters

ValueCountFrequency (%)
0 290851
96.9%
1 9238
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 300089
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 290851
96.9%
1 9238
 
3.1%

Most occurring scripts

ValueCountFrequency (%)
Common 300089
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 290851
96.9%
1 9238
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 300089
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 290851
96.9%
1 9238
 
3.1%

family_LINGERIE
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
0
291010 
1
 
9079

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters300089
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 291010
97.0%
1 9079
 
3.0%

Length

2025-03-23T15:43:17.817610image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-23T15:43:17.862797image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 291010
97.0%
1 9079
 
3.0%

Most occurring characters

ValueCountFrequency (%)
0 291010
97.0%
1 9079
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 300089
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 291010
97.0%
1 9079
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
Common 300089
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 291010
97.0%
1 9079
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 300089
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 291010
97.0%
1 9079
 
3.0%

family_LIQUOR,WINE,BEER
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
0
291219 
1
 
8870

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters300089
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 291219
97.0%
1 8870
 
3.0%

Length

2025-03-23T15:43:17.918094image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-23T15:43:17.962968image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 291219
97.0%
1 8870
 
3.0%

Most occurring characters

ValueCountFrequency (%)
0 291219
97.0%
1 8870
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 300089
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 291219
97.0%
1 8870
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
Common 300089
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 291219
97.0%
1 8870
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 300089
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 291219
97.0%
1 8870
 
3.0%

family_MAGAZINES
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
0
290971 
1
 
9118

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters300089
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 290971
97.0%
1 9118
 
3.0%

Length

2025-03-23T15:43:18.017651image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-23T15:43:18.063332image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 290971
97.0%
1 9118
 
3.0%

Most occurring characters

ValueCountFrequency (%)
0 290971
97.0%
1 9118
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 300089
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 290971
97.0%
1 9118
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
Common 300089
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 290971
97.0%
1 9118
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 300089
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 290971
97.0%
1 9118
 
3.0%

family_MEATS
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
0
290953 
1
 
9136

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters300089
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 290953
97.0%
1 9136
 
3.0%

Length

2025-03-23T15:43:18.118028image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-23T15:43:18.162930image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 290953
97.0%
1 9136
 
3.0%

Most occurring characters

ValueCountFrequency (%)
0 290953
97.0%
1 9136
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 300089
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 290953
97.0%
1 9136
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
Common 300089
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 290953
97.0%
1 9136
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 300089
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 290953
97.0%
1 9136
 
3.0%

family_PERSONAL CARE
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
0
290965 
1
 
9124

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters300089
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 290965
97.0%
1 9124
 
3.0%

Length

2025-03-23T15:43:18.218280image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-23T15:43:18.263047image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 290965
97.0%
1 9124
 
3.0%

Most occurring characters

ValueCountFrequency (%)
0 290965
97.0%
1 9124
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 300089
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 290965
97.0%
1 9124
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
Common 300089
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 290965
97.0%
1 9124
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 300089
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 290965
97.0%
1 9124
 
3.0%

family_PET SUPPLIES
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
0
290992 
1
 
9097

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters300089
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 290992
97.0%
1 9097
 
3.0%

Length

2025-03-23T15:43:18.318474image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-23T15:43:18.363592image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 290992
97.0%
1 9097
 
3.0%

Most occurring characters

ValueCountFrequency (%)
0 290992
97.0%
1 9097
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 300089
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 290992
97.0%
1 9097
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
Common 300089
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 290992
97.0%
1 9097
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 300089
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 290992
97.0%
1 9097
 
3.0%

family_PLAYERS AND ELECTRONICS
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
0
290851 
1
 
9238

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters300089
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 290851
96.9%
1 9238
 
3.1%

Length

2025-03-23T15:43:18.418134image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-23T15:43:18.463510image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 290851
96.9%
1 9238
 
3.1%

Most occurring characters

ValueCountFrequency (%)
0 290851
96.9%
1 9238
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 300089
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 290851
96.9%
1 9238
 
3.1%

Most occurring scripts

ValueCountFrequency (%)
Common 300089
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 290851
96.9%
1 9238
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 300089
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 290851
96.9%
1 9238
 
3.1%

family_POULTRY
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
0
290900 
1
 
9189

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters300089
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 290900
96.9%
1 9189
 
3.1%

Length

2025-03-23T15:43:18.518088image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-23T15:43:18.562758image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 290900
96.9%
1 9189
 
3.1%

Most occurring characters

ValueCountFrequency (%)
0 290900
96.9%
1 9189
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 300089
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 290900
96.9%
1 9189
 
3.1%

Most occurring scripts

ValueCountFrequency (%)
Common 300089
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 290900
96.9%
1 9189
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 300089
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 290900
96.9%
1 9189
 
3.1%

family_PREPARED FOODS
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
0
290904 
1
 
9185

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters300089
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 290904
96.9%
1 9185
 
3.1%

Length

2025-03-23T15:43:18.618383image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-23T15:43:18.663301image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 290904
96.9%
1 9185
 
3.1%

Most occurring characters

ValueCountFrequency (%)
0 290904
96.9%
1 9185
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 300089
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 290904
96.9%
1 9185
 
3.1%

Most occurring scripts

ValueCountFrequency (%)
Common 300089
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 290904
96.9%
1 9185
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 300089
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 290904
96.9%
1 9185
 
3.1%

family_PRODUCE
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
0
290976 
1
 
9113

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters300089
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 290976
97.0%
1 9113
 
3.0%

Length

2025-03-23T15:43:18.718706image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-23T15:43:18.763580image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 290976
97.0%
1 9113
 
3.0%

Most occurring characters

ValueCountFrequency (%)
0 290976
97.0%
1 9113
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 300089
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 290976
97.0%
1 9113
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
Common 300089
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 290976
97.0%
1 9113
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 300089
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 290976
97.0%
1 9113
 
3.0%

family_SCHOOL AND OFFICE SUPPLIES
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
0
290981 
1
 
9108

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters300089
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 290981
97.0%
1 9108
 
3.0%

Length

2025-03-23T15:43:18.818382image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-23T15:43:18.864055image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 290981
97.0%
1 9108
 
3.0%

Most occurring characters

ValueCountFrequency (%)
0 290981
97.0%
1 9108
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 300089
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 290981
97.0%
1 9108
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
Common 300089
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 290981
97.0%
1 9108
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 300089
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 290981
97.0%
1 9108
 
3.0%

family_SEAFOOD
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
0
291014 
1
 
9075

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters300089
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 291014
97.0%
1 9075
 
3.0%

Length

2025-03-23T15:43:18.918231image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-23T15:43:18.962565image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 291014
97.0%
1 9075
 
3.0%

Most occurring characters

ValueCountFrequency (%)
0 291014
97.0%
1 9075
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 300089
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 291014
97.0%
1 9075
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
Common 300089
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 291014
97.0%
1 9075
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 300089
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 291014
97.0%
1 9075
 
3.0%

is_holiday
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
0.0
292089 
1.0
 
8000

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters900267
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 292089
97.3%
1.0 8000
 
2.7%

Length

2025-03-23T15:43:19.017265image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-23T15:43:19.061655image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 292089
97.3%
1.0 8000
 
2.7%

Most occurring characters

ValueCountFrequency (%)
0 592178
65.8%
. 300089
33.3%
1 8000
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 600178
66.7%
Other Punctuation 300089
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 592178
98.7%
1 8000
 
1.3%
Other Punctuation
ValueCountFrequency (%)
. 300089
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 900267
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 592178
65.8%
. 300089
33.3%
1 8000
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 900267
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 592178
65.8%
. 300089
33.3%
1 8000
 
0.9%

is_event
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
0.0
290173 
1.0
 
9916

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters900267
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0 290173
96.7%
1.0 9916
 
3.3%

Length

2025-03-23T15:43:19.116250image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-23T15:43:19.161493image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 290173
96.7%
1.0 9916
 
3.3%

Most occurring characters

ValueCountFrequency (%)
0 590262
65.6%
. 300089
33.3%
1 9916
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 600178
66.7%
Other Punctuation 300089
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 590262
98.3%
1 9916
 
1.7%
Other Punctuation
ValueCountFrequency (%)
. 300089
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 900267
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 590262
65.6%
. 300089
33.3%
1 9916
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 900267
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 590262
65.6%
. 300089
33.3%
1 9916
 
1.1%

is_additional
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
0.0
294466 
1.0
 
5623

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters900267
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 294466
98.1%
1.0 5623
 
1.9%

Length

2025-03-23T15:43:19.216131image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-23T15:43:19.260457image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 294466
98.1%
1.0 5623
 
1.9%

Most occurring characters

ValueCountFrequency (%)
0 594555
66.0%
. 300089
33.3%
1 5623
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 600178
66.7%
Other Punctuation 300089
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 594555
99.1%
1 5623
 
0.9%
Other Punctuation
ValueCountFrequency (%)
. 300089
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 900267
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 594555
66.0%
. 300089
33.3%
1 5623
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 900267
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 594555
66.0%
. 300089
33.3%
1 5623
 
0.6%

is_transfer
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
0.0
298804 
1.0
 
1285

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters900267
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 298804
99.6%
1.0 1285
 
0.4%

Length

2025-03-23T15:43:19.315591image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-23T15:43:19.359728image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 298804
99.6%
1.0 1285
 
0.4%

Most occurring characters

ValueCountFrequency (%)
0 598893
66.5%
. 300089
33.3%
1 1285
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 600178
66.7%
Other Punctuation 300089
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 598893
99.8%
1 1285
 
0.2%
Other Punctuation
ValueCountFrequency (%)
. 300089
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 900267
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 598893
66.5%
. 300089
33.3%
1 1285
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 900267
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 598893
66.5%
. 300089
33.3%
1 1285
 
0.1%

is_bridge
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
0.0
299526 
1.0
 
563

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters900267
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 299526
99.8%
1.0 563
 
0.2%

Length

2025-03-23T15:43:19.414961image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-23T15:43:19.459327image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 299526
99.8%
1.0 563
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 599615
66.6%
. 300089
33.3%
1 563
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 600178
66.7%
Other Punctuation 300089
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 599615
99.9%
1 563
 
0.1%
Other Punctuation
ValueCountFrequency (%)
. 300089
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 900267
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 599615
66.6%
. 300089
33.3%
1 563
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 900267
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 599615
66.6%
. 300089
33.3%
1 563
 
0.1%

Interactions

2025-03-23T15:43:04.243275image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T15:42:55.268789image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T15:42:56.222022image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T15:42:57.590837image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T15:42:58.505124image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T15:42:59.435742image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T15:43:00.386589image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T15:43:01.470699image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T15:43:02.392593image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T15:43:03.268588image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T15:43:04.339433image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T15:42:55.374727image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T15:42:56.314097image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T15:42:57.683664image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T15:42:58.599377image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T15:42:59.533313image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T15:43:00.483062image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T15:43:01.562179image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T15:43:02.481320image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T15:43:03.364247image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T15:43:04.431355image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T15:42:55.465803image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T15:42:56.398639image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T15:42:57.770492image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T15:42:58.689849image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T15:42:59.626074image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T15:43:00.575711image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T15:43:01.653963image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T15:43:02.565763image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T15:43:03.455704image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T15:43:04.526944image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T15:42:55.558833image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T15:42:56.489578image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T15:42:57.860901image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T15:42:58.781417image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T15:42:59.722532image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T15:43:00.672051image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T15:43:01.747040image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T15:43:02.655134image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T15:43:03.551796image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T15:43:04.625230image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T15:42:55.652444image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T15:42:56.580467image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T15:42:57.953452image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T15:42:58.873747image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T15:42:59.819113image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T15:43:00.772741image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T15:43:01.840004image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T15:43:02.745830image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T15:43:03.650393image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T15:43:04.721948image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T15:42:55.755735image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T15:42:56.672099image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T15:42:58.048260image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T15:42:58.968665image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T15:42:59.914700image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T15:43:00.868561image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T15:43:01.935464image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T15:43:02.835847image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T15:43:03.747242image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T15:43:04.819002image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T15:42:55.850641image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T15:42:56.763314image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T15:42:58.142511image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T15:42:59.063710image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T15:43:00.012399image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T15:43:01.095967image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T15:43:02.027123image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T15:43:02.925770image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T15:43:03.859057image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T15:43:04.912446image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T15:42:55.943337image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T15:42:57.319749image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T15:42:58.235238image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T15:42:59.154060image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T15:43:00.106528image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T15:43:01.190662image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T15:43:02.115416image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T15:43:03.010728image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T15:43:03.962392image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T15:43:05.001814image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T15:42:56.030855image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T15:42:57.405200image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T15:42:58.319874image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T15:42:59.241635image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T15:43:00.195048image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T15:43:01.279684image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T15:43:02.208040image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T15:43:03.090586image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T15:43:04.050899image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T15:43:05.094594image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T15:42:56.127222image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T15:42:57.497581image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T15:42:58.413117image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T15:42:59.337278image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T15:43:00.290722image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T15:43:01.376451image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T15:43:02.299911image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T15:43:03.178851image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-23T15:43:04.146466image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-03-23T15:43:19.618006image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
citycity_Babahoyocity_Cayambecity_Cuencacity_Daulecity_El Carmencity_Esmeraldascity_Guarandacity_Guayaquilcity_Ibarracity_Latacungacity_Libertadcity_Lojacity_Machalacity_Mantacity_Playascity_Puyocity_Quevedocity_Quitocity_Riobambacity_Salinascity_Santo Domingoclustercluster.1days_after_earthquakedays_from_previous_paydaydays_to_next_paydaydcoilwticofamilyfamily_BABY CAREfamily_BEAUTYfamily_BEVERAGESfamily_BOOKSfamily_BREAD/BAKERYfamily_CELEBRATIONfamily_CLEANINGfamily_DAIRYfamily_DELIfamily_EGGSfamily_FROZEN FOODSfamily_GROCERY Ifamily_GROCERY IIfamily_HARDWAREfamily_HOME AND KITCHEN Ifamily_HOME AND KITCHEN IIfamily_HOME APPLIANCESfamily_HOME CAREfamily_LADIESWEARfamily_LAWN AND GARDENfamily_LINGERIEfamily_LIQUOR,WINE,BEERfamily_MAGAZINESfamily_MEATSfamily_PERSONAL CAREfamily_PET SUPPLIESfamily_PLAYERS AND ELECTRONICSfamily_POULTRYfamily_PREPARED FOODSfamily_PRODUCEfamily_SCHOOL AND OFFICE SUPPLIESfamily_SEAFOODidis_additionalis_bridgeis_eventis_holidayis_paydayis_transferonpromotionsalesstatestate_Bolivarstate_Chimborazostate_Cotopaxistate_El Orostate_Esmeraldasstate_Guayasstate_Imbaburastate_Lojastate_Los Riosstate_Manabistate_Pastazastate_Pichinchastate_Santa Elenastate_Santo Domingo de los Tsachilasstate_Tungurahuastore_nbrtypetype_Btype_Ctype_Dtype_E
city1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.5950.5950.0000.0000.0000.0000.0000.0000.0000.0050.0000.0060.0000.0000.0010.0050.0040.0000.0000.0000.0050.0000.0000.0050.0000.0000.0000.0000.0000.0000.0000.0000.0040.0030.0060.0000.0000.0000.0000.0000.0100.0000.0000.0100.0000.0030.0160.0371.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.7220.6340.5520.7680.5320.772
city_Babahoyo1.0001.0000.0180.0330.0180.0180.0180.0180.0560.0180.0260.0180.0190.0260.0260.0180.0180.0180.0950.0180.0180.0330.3820.3820.0000.0000.0000.0010.0030.0000.0000.0010.0010.0020.0000.0000.0000.0050.0030.0000.0000.0000.0000.0000.0020.0020.0000.0020.0000.0010.0000.0000.0000.0000.0010.0020.0000.0000.0000.0000.0010.0000.0000.0010.0000.0000.0000.0000.0100.0070.6990.0180.0180.0260.0260.0180.0680.0180.0190.6990.0330.0180.0990.0180.0330.0260.4250.3230.3230.0830.0950.038
city_Cayambe1.0000.0181.0000.0330.0190.0180.0190.0190.0570.0190.0270.0190.0190.0270.0270.0190.0190.0180.0970.0190.0190.0330.3270.3270.0010.0000.0010.0040.0000.0000.0000.0000.0000.0000.0000.0030.0010.0000.0030.0000.0000.0000.0000.0000.0040.0000.0000.0000.0000.0000.0000.0000.0000.0010.0010.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0100.0130.1860.0190.0190.0270.0270.0190.0690.0190.0190.0260.0330.0190.1860.0190.0330.0270.4290.3270.3270.0850.0970.039
city_Cuenca1.0000.0330.0331.0000.0330.0330.0330.0330.1010.0330.0480.0330.0340.0470.0480.0330.0330.0330.1720.0330.0330.0590.5180.5180.0040.0000.0040.0020.0000.0000.0000.0000.0000.0000.0000.0020.0000.0000.0000.0000.0000.0000.0010.0000.0000.0000.0000.0020.0000.0000.0000.0000.0000.0010.0000.0000.0000.0000.0000.0000.0020.0040.0020.0000.0000.0000.0030.0020.0090.0171.0000.0330.0330.0480.0470.0330.1230.0330.0340.0470.0590.0330.1790.0330.0590.0480.5310.2520.1240.1500.1730.069
city_Daule1.0000.0180.0190.0331.0000.0190.0190.0190.0570.0190.0270.0190.0190.0270.0270.0190.0190.0180.0970.0190.0190.0340.4290.4290.0040.0010.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0010.0000.0030.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0040.0000.0000.0000.0000.0050.0000.0000.0000.0000.0000.0000.0040.0110.2720.0190.0190.0270.0270.0190.2720.0190.0190.0260.0330.0190.1010.0190.0340.0270.4290.1940.0570.0850.1940.039
city_El Carmen1.0000.0180.0180.0330.0191.0000.0190.0190.0570.0190.0270.0190.0190.0260.0270.0190.0190.0180.0960.0190.0190.0330.2870.2870.0020.0030.0020.0040.0000.0030.0000.0020.0000.0010.0020.0000.0000.0000.0010.0000.0000.0000.0030.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0030.0000.0010.0050.0000.0000.0010.0000.0010.0000.0300.0090.5640.0190.0190.0270.0260.0190.0690.0190.0190.0260.5640.0190.1000.0190.0330.0270.3880.2210.0570.2210.0970.039
city_Esmeraldas1.0000.0180.0190.0330.0190.0191.0000.0190.0570.0190.0270.0190.0190.0270.0270.0190.0190.0180.0970.0190.0190.0340.3890.3890.0000.0000.0000.0000.0000.0000.0000.0010.0000.0020.0000.0000.0030.0010.0000.0000.0000.0000.0020.0020.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0020.0000.0010.0010.0000.0000.0000.0040.0000.0000.0000.0000.0000.0000.0020.0091.0000.0190.0190.0270.0271.0000.0690.0190.0190.0260.0330.0190.1010.0190.0340.0270.4320.4850.0570.0850.0970.485
city_Guaranda1.0000.0180.0190.0330.0190.0190.0191.0000.0570.0190.0270.0190.0190.0270.0270.0190.0190.0180.0970.0190.0190.0340.3070.3070.0000.0000.0020.0030.0030.0000.0000.0020.0000.0020.0020.0000.0000.0000.0030.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0040.0000.0000.0000.0000.0050.0000.0000.0000.0030.0000.0000.0020.0000.0010.0000.0030.0000.0000.0020.0111.0001.0000.0190.0270.0270.0190.0690.0190.0190.0260.0330.0190.1010.0190.0340.0270.3860.2220.0570.2220.0970.039
city_Guayaquil1.0000.0560.0570.1010.0570.0570.0570.0571.0000.0570.0820.0570.0580.0810.0820.0570.0570.0560.2940.0570.0570.1020.4750.4750.0040.0000.0000.0050.0000.0000.0000.0000.0020.0000.0010.0010.0000.0030.0000.0000.0000.0000.0000.0000.0000.0030.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0030.0070.0000.0000.0020.0000.0020.0090.0270.8230.0570.0570.0820.0810.0570.8230.0570.0580.0800.1010.0570.3060.0570.1020.0820.6810.2810.0260.0260.0720.279
city_Ibarra1.0000.0180.0190.0330.0190.0190.0190.0190.0571.0000.0270.0190.0190.0270.0270.0190.0190.0180.0970.0190.0190.0340.3070.3070.0000.0000.0030.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0010.0000.0000.0000.0000.0000.0000.0000.0000.0000.0010.0000.0000.0000.0020.0000.0020.0000.0000.0000.0030.0000.0000.0000.0000.0000.0040.0111.0000.0190.0190.0270.0270.0190.0691.0000.0190.0260.0330.0190.1010.0190.0340.0270.4290.2220.0570.2220.0970.039
city_Latacunga1.0000.0260.0270.0480.0270.0270.0270.0270.0820.0271.0000.0270.0270.0380.0390.0270.0270.0270.1390.0270.0270.0480.4390.4390.0030.0000.0000.0060.0000.0000.0010.0050.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0020.0000.0020.0000.0000.0020.0000.0000.0000.0000.0030.0010.0000.0000.0000.0000.0010.0030.0000.0020.0070.0000.0000.0060.0161.0000.0270.0271.0000.0380.0270.0990.0270.0270.0380.0480.0270.1450.0270.0480.0390.6150.3180.0820.3180.1390.056
city_Libertad1.0000.0180.0190.0330.0190.0190.0190.0190.0570.0190.0271.0000.0190.0270.0270.0190.0190.0190.0980.0190.0190.0340.3920.3920.0020.0000.0000.0000.0000.0000.0000.0000.0000.0000.0040.0000.0000.0000.0030.0000.0000.0000.0000.0030.0000.0020.0000.0010.0000.0000.0030.0010.0000.0010.0000.0000.0000.0000.0000.0000.0000.0030.0000.0000.0000.0000.0000.0000.0080.0100.2730.0190.0190.0270.0270.0190.2730.0190.0190.0270.0330.0190.1020.0190.0340.0270.3880.4880.0580.0850.0980.488
city_Loja1.0000.0190.0190.0340.0190.0190.0190.0190.0580.0190.0270.0191.0000.0270.0270.0190.0190.0190.0990.0190.0190.0340.2930.2930.0030.0020.0000.0000.0030.0020.0000.0020.0000.0000.0000.0000.0000.0030.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0020.0000.0000.0030.0050.0010.0000.0000.0000.0000.0020.0020.0010.0000.0020.0020.0010.0091.0000.0190.0190.0270.0270.0190.0710.0191.0000.0270.0340.0190.1030.0190.0340.0270.3930.1970.0580.0860.1970.039
city_Machala1.0000.0260.0270.0470.0270.0260.0270.0270.0810.0270.0380.0270.0271.0000.0380.0270.0270.0260.1380.0270.0270.0480.4090.4090.0000.0000.0010.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0040.0000.0020.0030.0000.0030.0000.0000.0000.0000.0000.0000.0020.0000.0000.0010.0000.0000.0020.0000.0000.0000.0000.0000.0000.0030.0000.0000.0000.0000.0030.0000.0141.0000.0270.0270.0381.0000.0270.0980.0270.0270.0370.0470.0270.1430.0270.0480.0380.6120.1570.0810.0970.0680.055
city_Manta1.0000.0260.0270.0480.0270.0270.0270.0270.0820.0270.0390.0270.0270.0381.0000.0270.0270.0260.1390.0270.0270.0480.4710.4710.0000.0000.0000.0000.0000.0000.0000.0000.0010.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0010.0000.0000.0010.0010.0000.0030.0020.0010.0040.0010.0000.0000.0000.0000.0000.0000.0000.0000.0020.0000.0010.0000.0220.0140.8100.0270.0270.0390.0380.0270.0990.0270.0270.0380.8100.0270.1440.0270.0480.0390.5570.2210.0820.1210.0670.055
city_Playas1.0000.0180.0190.0330.0190.0190.0190.0190.0570.0190.0270.0190.0190.0270.0271.0000.0190.0190.0980.0190.0190.0340.2900.2900.0000.0020.0000.0030.0000.0000.0000.0030.0000.0050.0000.0010.0000.0010.0020.0000.0000.0000.0000.0000.0010.0010.0010.0000.0000.0000.0000.0000.0000.0000.0000.0010.0020.0000.0010.0000.0000.0000.0010.0000.0000.0030.0000.0000.0130.0110.2730.0190.0190.0270.0270.0190.2730.0190.0190.0270.0330.0190.1020.0190.0340.0270.3890.2230.0580.2230.0980.039
city_Puyo1.0000.0180.0190.0330.0190.0190.0190.0190.0570.0190.0270.0190.0190.0270.0270.0191.0000.0180.0970.0190.0190.0340.3280.3280.0000.0000.0000.0000.0020.0010.0000.0010.0040.0000.0000.0000.0020.0020.0000.0000.0000.0010.0000.0000.0000.0000.0030.0000.0000.0000.0000.0000.0000.0020.0000.0000.0010.0000.0000.0020.0000.0000.0010.0000.0040.0000.0000.0000.0000.0111.0000.0190.0190.0270.0270.0190.0690.0190.0190.0260.0331.0000.1010.0190.0340.0270.3860.2220.0570.2220.0970.039
city_Quevedo1.0000.0180.0180.0330.0180.0180.0180.0180.0560.0180.0270.0190.0190.0260.0260.0190.0181.0000.0960.0180.0180.0330.2850.2850.0000.0000.0040.0000.0050.0000.0040.0000.0010.0030.0000.0000.0000.0000.0020.0000.0000.0000.0040.0000.0020.0000.0000.0000.0010.0020.0000.0030.0000.0000.0000.0000.0000.0000.0000.0000.0020.0000.0030.0000.0000.0000.0020.0000.0070.0110.7030.0180.0180.0270.0260.0180.0690.0180.0190.7030.0330.0180.1000.0180.0330.0270.3810.2190.0570.2190.0960.038
city_Quito1.0000.0950.0970.1720.0970.0960.0970.0970.2940.0970.1390.0980.0990.1380.1390.0980.0970.0961.0000.0970.0970.1730.6740.6740.0060.0000.0000.0060.0030.0000.0000.0000.0000.0000.0000.0030.0000.0000.0000.0000.0000.0020.0040.0030.0000.0010.0020.0000.0000.0000.0020.0000.0010.0010.0030.0000.0020.0000.0000.0000.0020.0010.0060.0000.0000.0030.0000.0020.0190.0960.9600.0970.0970.1390.1380.0970.3570.0970.0990.1360.1710.0970.9600.0970.1730.1390.8170.4190.0400.2620.0800.200
city_Riobamba1.0000.0180.0190.0330.0190.0190.0190.0190.0570.0190.0270.0190.0190.0270.0270.0190.0190.0180.0971.0000.0190.0330.3270.3270.0030.0000.0000.0000.0000.0000.0000.0000.0010.0010.0020.0000.0030.0000.0000.0000.0020.0020.0000.0000.0000.0000.0000.0000.0000.0000.0020.0030.0000.0010.0000.0030.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0101.0000.0191.0000.0270.0270.0190.0690.0190.0190.0260.0330.0190.1010.0190.0330.0270.4280.2210.0570.2210.0970.039
city_Salinas1.0000.0180.0190.0330.0190.0190.0190.0190.0570.0190.0270.0190.0190.0270.0270.0190.0190.0180.0970.0191.0000.0340.4300.4300.0000.0000.0000.0000.0000.0000.0000.0000.0000.0030.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0020.0000.0000.0000.0000.0000.0000.0000.0010.0000.0000.0000.0000.0000.0020.0020.0050.0101.0000.0190.0190.0270.0270.0190.0700.0190.0190.0260.0330.0190.1011.0000.0340.0270.4290.1940.0570.0850.1940.039
city_Santo Domingo1.0000.0330.0330.0590.0340.0330.0340.0340.1020.0340.0480.0340.0340.0480.0480.0340.0340.0330.1730.0330.0341.0000.3580.3580.0000.0020.0000.0000.0000.0000.0000.0000.0000.0000.0000.0020.0030.0000.0000.0000.0000.0000.0020.0000.0000.0000.0000.0000.0000.0000.0000.0020.0000.0020.0000.0020.0020.0010.0000.0000.0020.0000.0000.0000.0010.0060.0000.0000.0070.0191.0000.0340.0330.0480.0480.0340.1240.0340.0340.0470.0590.0340.1800.0341.0000.0480.3620.1720.1300.0290.0000.069
cluster0.5950.3820.3270.5180.4290.2870.3890.3070.4750.3070.4390.3920.2930.4090.4710.2900.3280.2850.6740.3270.4300.3581.0001.0000.0020.001-0.0010.0020.0000.0010.0010.0050.0000.0030.0000.0060.0000.0000.0000.0000.0000.0040.0000.0030.0040.0000.0000.0000.0000.0000.0050.0000.0000.0000.0020.0000.0010.0000.0000.0000.0030.0000.0010.0000.0000.0030.0040.0000.0040.0340.5190.3070.3270.4390.4090.3890.5610.3070.2930.3110.3920.3280.6620.4300.3580.354-0.0650.7630.7680.6250.8680.802
cluster.10.5950.3820.3270.5180.4290.2870.3890.3070.4750.3070.4390.3920.2930.4090.4710.2900.3280.2850.6740.3270.4300.3581.0001.0000.0020.001-0.0010.0020.0000.0010.0010.0050.0000.0030.0000.0060.0000.0000.0000.0000.0000.0040.0000.0030.0040.0000.0000.0000.0000.0000.0050.0000.0000.0000.0020.0000.0010.0000.0000.0000.0030.0000.0010.0000.0000.0030.0040.0000.0040.0340.5190.3070.3270.4390.4090.3890.5610.3070.2930.3110.3920.3280.6620.4300.3580.354-0.0650.7630.7680.6250.8680.802
days_after_earthquake0.0000.0000.0010.0040.0040.0020.0000.0000.0040.0000.0030.0020.0030.0000.0000.0000.0000.0000.0060.0030.0000.0000.0020.0021.0000.003-0.006-0.3910.0000.0000.0000.0020.0000.0000.0000.0000.0000.0000.0000.0030.0030.0040.0000.0020.0010.0040.0000.0000.0040.0000.0000.0050.0000.0030.0000.0000.0000.0000.0000.0000.0020.8020.1510.0890.0950.0700.0220.0960.3650.1520.0000.0000.0030.0030.0000.0000.0040.0000.0030.0000.0000.0000.0050.0000.0000.000-0.0000.0020.0040.0000.0030.002
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family_GROCERY II0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0010.0000.0020.0020.0000.0000.0040.0040.0040.0000.0000.0001.0000.0310.0310.0310.0310.0310.0310.0310.0310.0310.0310.0310.0311.0000.0310.0310.0310.0310.0310.0310.0310.0310.0310.0310.0310.0310.0310.0310.0310.0310.0310.0310.0310.0020.0000.0000.0020.0000.0000.0000.0120.0140.0000.0000.0020.0000.0000.0000.0000.0000.0000.0000.0000.0010.0010.0000.0000.0000.0030.0030.0000.0030.0040.000
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family_HOME AND KITCHEN II0.0000.0020.0040.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0010.0000.0020.0000.0000.0000.0000.0040.0040.0010.0000.0000.0011.0000.0310.0310.0300.0310.0310.0310.0310.0310.0310.0310.0310.0310.0310.0310.0311.0000.0310.0310.0310.0310.0310.0300.0310.0310.0310.0310.0310.0310.0310.0310.0310.0310.0000.0000.0000.0000.0000.0000.0000.0120.0140.0000.0000.0000.0000.0000.0000.0000.0000.0000.0040.0000.0000.0020.0000.0000.0000.0000.0030.0020.0040.0000.000
family_HOME APPLIANCES0.0050.0020.0000.0000.0030.0000.0000.0000.0030.0000.0020.0020.0000.0000.0010.0010.0000.0000.0010.0000.0000.0000.0000.0000.0040.0000.0000.0021.0000.0310.0310.0310.0310.0310.0310.0310.0310.0310.0310.0310.0310.0310.0310.0310.0311.0000.0310.0310.0310.0310.0310.0310.0310.0310.0310.0310.0310.0310.0310.0310.0310.0000.0000.0000.0000.0000.0000.0000.0120.0150.0000.0000.0000.0020.0000.0000.0000.0000.0000.0000.0000.0000.0010.0000.0000.0050.0020.0020.0030.0010.0000.000
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family_LIQUOR,WINE,BEER0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0020.0030.0000.0000.0000.0000.0000.0000.0020.0020.0000.0000.0050.0050.0000.0020.0000.0001.0000.0310.0310.0300.0310.0310.0310.0310.0310.0310.0310.0310.0310.0310.0310.0310.0300.0310.0310.0310.0310.0311.0000.0310.0310.0310.0310.0310.0310.0310.0310.0310.0310.0020.0000.0000.0030.0000.0000.0020.0120.0140.0000.0000.0020.0020.0000.0000.0000.0000.0000.0010.0000.0000.0020.0000.0000.0000.0000.0020.0000.0020.0020.001
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family_PREPARED FOODS0.0000.0000.0000.0000.0000.0000.0010.0000.0000.0000.0000.0000.0010.0000.0000.0000.0000.0000.0000.0000.0000.0010.0000.0000.0000.0030.0000.0001.0000.0310.0310.0310.0310.0310.0310.0320.0320.0310.0320.0310.0310.0310.0310.0310.0310.0310.0320.0310.0320.0310.0310.0310.0310.0310.0310.0320.0311.0000.0310.0310.0310.0000.0000.0040.0000.0030.0000.0000.0120.0150.0000.0000.0000.0000.0000.0010.0000.0000.0010.0000.0000.0000.0000.0000.0010.0010.0000.0000.0000.0010.0010.000
family_PRODUCE0.0000.0000.0000.0000.0000.0030.0000.0030.0000.0020.0000.0000.0000.0000.0000.0010.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0310.0310.0310.0310.0310.0310.0310.0310.0310.0310.0310.0310.0310.0310.0310.0310.0310.0310.0310.0310.0310.0310.0310.0310.0310.0310.0310.0310.0311.0000.0310.0310.0010.0000.0000.0000.0020.0000.0000.1990.0860.0000.0030.0000.0000.0000.0000.0000.0020.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
family_SCHOOL AND OFFICE SUPPLIES0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0020.0000.0000.0000.0000.0000.0000.0000.0000.0050.0050.0001.0000.0310.0310.0310.0310.0310.0310.0310.0310.0310.0310.0310.0310.0310.0310.0310.0310.0310.0310.0310.0310.0310.0310.0310.0310.0310.0310.0310.0310.0310.0311.0000.0310.0050.0000.0000.0000.0000.0020.0000.0120.0140.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0020.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
family_SEAFOOD0.0000.0010.0000.0020.0000.0010.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0020.0020.0000.0010.0020.0030.0030.0020.0000.0000.0001.0000.0310.0310.0310.0310.0310.0310.0310.0310.0310.0310.0310.0310.0310.0310.0310.0310.0310.0310.0310.0310.0310.0310.0310.0310.0310.0310.0310.0310.0310.0310.0311.0000.0050.0020.0000.0000.0000.0010.0000.0120.0140.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0020.0010.0020.0000.0010.0030.0020.0030.0020.000
id0.0000.0000.0000.0040.0050.0050.0040.0020.0030.0000.0010.0030.0000.0000.0000.0000.0000.0000.0010.0000.0000.0000.0000.0000.8020.008-0.009-0.7440.0000.0000.0000.0000.0000.0000.0040.0000.0000.0040.0000.0000.0000.0020.0000.0000.0000.0000.0010.0000.0000.0020.0020.0000.0050.0000.0030.0010.0000.0000.0010.0050.0051.0000.1270.0900.3150.0530.0070.0760.4040.2170.0000.0020.0000.0010.0000.0040.0030.0000.0000.0000.0000.0000.0020.0000.0000.001-0.0010.0020.0010.0000.0000.003
is_additional0.0100.0000.0000.0020.0000.0000.0000.0000.0070.0030.0030.0000.0020.0030.0000.0010.0010.0030.0060.0000.0000.0000.0010.0010.1510.1140.1270.1420.0000.0000.0000.0000.0000.0030.0010.0000.0000.0000.0030.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0010.0010.0000.0020.0000.0000.0000.0020.1271.0000.0930.0000.0200.0420.0030.0100.0100.0100.0000.0000.0030.0030.0000.0040.0030.0020.0030.0000.0010.0060.0000.0000.0040.0090.0020.0000.0030.0000.000
is_bridge0.0000.0010.0000.0000.0000.0000.0000.0010.0000.0000.0000.0000.0020.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0890.0690.0670.0700.0000.0000.0000.0020.0000.0000.0000.0000.0000.0000.0000.0000.0050.0000.0010.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0010.0000.0000.0000.0040.0000.0000.0000.0900.0931.0000.0080.0070.0110.0010.0040.0000.0000.0010.0000.0000.0000.0000.0000.0000.0020.0000.0000.0000.0000.0000.0000.0000.0040.0000.0000.0010.0010.000
is_event0.0000.0000.0000.0000.0000.0010.0000.0000.0000.0000.0020.0000.0010.0000.0020.0000.0040.0000.0000.0000.0000.0010.0000.0000.0950.0640.0640.1470.0040.0020.0030.0000.0000.0030.0000.0010.0000.0000.0000.0010.0020.0020.0000.0000.0000.0000.0020.0000.0010.0030.0030.0000.0020.0000.0000.0000.0000.0000.0000.0000.0000.3150.0000.0081.0000.0050.0150.0120.0230.0140.0030.0000.0000.0020.0000.0000.0000.0000.0010.0000.0030.0040.0000.0000.0010.0000.0000.0010.0010.0030.0010.000
is_holiday0.0100.0000.0000.0000.0000.0000.0000.0030.0020.0000.0070.0000.0000.0000.0000.0030.0000.0000.0030.0000.0000.0060.0030.0030.0700.1260.1230.0590.0040.0010.0000.0000.0000.0030.0000.0010.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0020.0030.0000.0000.0000.0030.0000.0000.0030.0000.0030.0020.0000.0000.0530.0200.0070.0051.0000.0270.0110.0000.0090.0100.0030.0000.0070.0000.0000.0030.0000.0000.0000.0000.0000.0020.0000.0060.0020.0080.0000.0000.0020.0000.001
is_payday0.0000.0000.0000.0030.0000.0010.0000.0000.0000.0000.0000.0000.0020.0000.0010.0000.0000.0020.0000.0000.0020.0000.0040.0040.0220.6790.6800.0230.0000.0000.0020.0000.0000.0000.0030.0010.0000.0000.0010.0000.0030.0000.0000.0010.0000.0000.0000.0000.0000.0010.0000.0000.0040.0020.0000.0000.0000.0000.0000.0020.0010.0070.0420.0110.0150.0271.0000.0170.0000.0000.0020.0000.0000.0000.0000.0000.0000.0000.0020.0010.0000.0000.0000.0020.0000.0020.0000.0000.0000.0010.0020.000
is_transfer0.0030.0000.0000.0020.0000.0000.0000.0000.0020.0000.0000.0000.0020.0030.0000.0000.0000.0000.0020.0000.0020.0000.0000.0000.0960.1150.1060.0510.0020.0000.0000.0000.0000.0000.0010.0020.0000.0030.0000.0010.0000.0000.0000.0030.0000.0000.0000.0020.0000.0000.0020.0040.0000.0000.0000.0000.0000.0000.0000.0000.0000.0760.0030.0010.0120.0110.0171.0000.0000.0040.0050.0000.0000.0000.0030.0000.0020.0000.0020.0000.0000.0000.0030.0020.0000.0000.0000.0000.0000.0000.0000.000
onpromotion0.0160.0100.0100.0090.0040.0300.0020.0020.0090.0040.0060.0080.0010.0000.0220.0130.0000.0070.0190.0000.0050.0070.0040.0040.3650.000-0.001-0.2790.0940.0120.0120.0180.0120.0120.0120.0110.0500.0070.0120.0120.1880.0120.0120.0120.0120.0120.0120.0120.0120.0120.0120.0120.0120.0110.0120.0120.0120.0120.1990.0120.0120.4040.0100.0040.0230.0000.0000.0001.0000.5390.0120.0020.0000.0060.0000.0020.0110.0040.0010.0130.0250.0000.0170.0050.0070.0070.0090.0220.0250.0230.0280.015
sales0.0370.0070.0130.0170.0110.0090.0090.0110.0270.0110.0160.0100.0090.0140.0140.0110.0110.0110.0960.0100.0100.0190.0340.0340.152-0.001-0.001-0.1810.1150.0150.0140.1120.0140.0140.0140.0140.0150.0140.0150.0140.2640.0140.0140.0140.0140.0150.0150.0140.0150.0140.0140.0150.0140.0150.0140.0150.0150.0150.0860.0140.0140.2170.0100.0000.0140.0090.0000.0040.5391.0000.0370.0110.0100.0160.0140.0090.0360.0110.0090.0130.0180.0110.0980.0100.0190.003-0.0170.0610.0120.0510.0270.020
state1.0000.6990.1861.0000.2720.5641.0001.0000.8231.0001.0000.2731.0001.0000.8100.2731.0000.7030.9601.0001.0001.0000.5190.5190.0000.0000.0000.0000.0000.0000.0000.0050.0020.0050.0000.0000.0030.0050.0000.0000.0000.0000.0040.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0040.0040.0040.0000.0000.0000.0000.0000.0100.0000.0030.0100.0020.0050.0120.0371.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.6590.5190.3650.6470.4480.641
state_Bolivar1.0000.0180.0190.0330.0190.0190.0191.0000.0570.0190.0270.0190.0190.0270.0270.0190.0190.0180.0970.0190.0190.0340.3070.3070.0000.0000.0020.0030.0030.0000.0000.0020.0000.0020.0020.0000.0000.0000.0030.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0040.0000.0000.0000.0000.0050.0000.0000.0000.0030.0000.0000.0020.0000.0010.0000.0030.0000.0000.0020.0111.0001.0000.0190.0270.0270.0190.0690.0190.0190.0260.0330.0190.1010.0190.0340.0270.3860.2220.0570.2220.0970.039
state_Chimborazo1.0000.0180.0190.0330.0190.0190.0190.0190.0570.0190.0270.0190.0190.0270.0270.0190.0190.0180.0971.0000.0190.0330.3270.3270.0030.0000.0000.0000.0000.0000.0000.0000.0010.0010.0020.0000.0030.0000.0000.0000.0020.0020.0000.0000.0000.0000.0000.0000.0000.0000.0020.0030.0000.0010.0000.0030.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0101.0000.0191.0000.0270.0270.0190.0690.0190.0190.0260.0330.0190.1010.0190.0330.0270.4280.2210.0570.2210.0970.039
state_Cotopaxi1.0000.0260.0270.0480.0270.0270.0270.0270.0820.0271.0000.0270.0270.0380.0390.0270.0270.0270.1390.0270.0270.0480.4390.4390.0030.0000.0000.0060.0000.0000.0010.0050.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0020.0000.0020.0000.0000.0020.0000.0000.0000.0000.0030.0010.0000.0000.0000.0000.0010.0030.0000.0020.0070.0000.0000.0060.0161.0000.0270.0271.0000.0380.0270.0990.0270.0270.0380.0480.0270.1450.0270.0480.0390.6150.3180.0820.3180.1390.056
state_El Oro1.0000.0260.0270.0470.0270.0260.0270.0270.0810.0270.0380.0270.0271.0000.0380.0270.0270.0260.1380.0270.0270.0480.4090.4090.0000.0000.0010.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0040.0000.0020.0030.0000.0030.0000.0000.0000.0000.0000.0000.0020.0000.0000.0010.0000.0000.0020.0000.0000.0000.0000.0000.0000.0030.0000.0000.0000.0000.0030.0000.0141.0000.0270.0270.0381.0000.0270.0980.0270.0270.0370.0470.0270.1430.0270.0480.0380.6120.1570.0810.0970.0680.055
state_Esmeraldas1.0000.0180.0190.0330.0190.0191.0000.0190.0570.0190.0270.0190.0190.0270.0270.0190.0190.0180.0970.0190.0190.0340.3890.3890.0000.0000.0000.0000.0000.0000.0000.0010.0000.0020.0000.0000.0030.0010.0000.0000.0000.0000.0020.0020.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0020.0000.0010.0010.0000.0000.0000.0040.0000.0000.0000.0000.0000.0000.0020.0091.0000.0190.0190.0270.0271.0000.0690.0190.0190.0260.0330.0190.1010.0190.0340.0270.4320.4850.0570.0850.0970.485
state_Guayas1.0000.0680.0690.1230.2720.0690.0690.0690.8230.0690.0990.2730.0710.0980.0990.2730.0690.0690.3570.0690.0700.1240.5610.5610.0040.0000.0000.0050.0000.0000.0000.0010.0020.0000.0000.0000.0000.0050.0000.0000.0000.0000.0000.0030.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0030.0040.0000.0000.0030.0000.0020.0110.0361.0000.0690.0690.0990.0980.0691.0000.0690.0710.0980.1220.0690.3720.0700.1240.1000.7120.3930.0810.0050.0640.384
state_Imbabura1.0000.0180.0190.0330.0190.0190.0190.0190.0571.0000.0270.0190.0190.0270.0270.0190.0190.0180.0970.0190.0190.0340.3070.3070.0000.0000.0030.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0010.0000.0000.0000.0000.0000.0000.0000.0000.0000.0010.0000.0000.0000.0020.0000.0020.0000.0000.0000.0030.0000.0000.0000.0000.0000.0040.0111.0000.0190.0190.0270.0270.0190.0691.0000.0190.0260.0330.0190.1010.0190.0340.0270.4290.2220.0570.2220.0970.039
state_Loja1.0000.0190.0190.0340.0190.0190.0190.0190.0580.0190.0270.0191.0000.0270.0270.0190.0190.0190.0990.0190.0190.0340.2930.2930.0030.0020.0000.0000.0030.0020.0000.0020.0000.0000.0000.0000.0000.0030.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0020.0000.0000.0030.0050.0010.0000.0000.0000.0000.0020.0020.0010.0000.0020.0020.0010.0091.0000.0190.0190.0270.0270.0190.0710.0191.0000.0270.0340.0190.1030.0190.0340.0270.3930.1970.0580.0860.1970.039
state_Los Rios1.0000.6990.0260.0470.0260.0260.0260.0260.0800.0260.0380.0270.0270.0370.0380.0270.0260.7030.1360.0260.0260.0470.3110.3110.0000.0020.0050.0000.0060.0000.0020.0010.0020.0040.0000.0000.0000.0030.0040.0000.0000.0000.0020.0000.0040.0000.0000.0000.0010.0000.0010.0020.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0030.0000.0000.0000.0010.0000.0130.0131.0000.0260.0260.0380.0370.0260.0980.0260.0271.0000.0470.0260.1420.0260.0470.0380.3840.2420.1890.0970.1370.055
state_Manabi1.0000.0330.0330.0590.0330.5640.0330.0330.1010.0330.0480.0330.0340.0470.8100.0330.0330.0330.1710.0330.0330.0590.3920.3920.0000.0000.0010.0000.0000.0020.0000.0000.0000.0010.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0020.0010.0020.0040.0010.0000.0000.0000.0000.0000.0000.0000.0000.0030.0000.0000.0000.0250.0181.0000.0330.0330.0480.0470.0330.1220.0330.0340.0471.0000.0330.1780.0330.0590.0480.6870.1550.1010.0290.0000.068
state_Pastaza1.0000.0180.0190.0330.0190.0190.0190.0190.0570.0190.0270.0190.0190.0270.0270.0191.0000.0180.0970.0190.0190.0340.3280.3280.0000.0000.0000.0000.0020.0010.0000.0010.0040.0000.0000.0000.0020.0020.0000.0000.0000.0010.0000.0000.0000.0000.0030.0000.0000.0000.0000.0000.0000.0020.0000.0000.0010.0000.0000.0020.0000.0000.0010.0000.0040.0000.0000.0000.0000.0111.0000.0190.0190.0270.0270.0190.0690.0190.0190.0260.0331.0000.1010.0190.0340.0270.3860.2220.0570.2220.0970.039
state_Pichincha1.0000.0990.1860.1790.1010.1000.1010.1010.3060.1010.1450.1020.1030.1430.1440.1020.1010.1000.9600.1010.1010.1800.6620.6620.0050.0000.0000.0050.0030.0000.0000.0000.0000.0000.0000.0020.0000.0000.0010.0000.0000.0010.0040.0030.0020.0010.0020.0000.0000.0000.0020.0000.0000.0020.0020.0000.0020.0000.0000.0000.0020.0020.0060.0000.0000.0020.0000.0030.0170.0981.0000.1010.1010.1450.1430.1010.3720.1010.1030.1420.1780.1011.0000.1010.1800.1450.8610.4320.1320.2820.0510.208
state_Santa Elena1.0000.0180.0190.0330.0190.0190.0190.0190.0570.0190.0270.0190.0190.0270.0270.0190.0190.0180.0970.0191.0000.0340.4300.4300.0000.0000.0000.0000.0000.0000.0000.0000.0000.0030.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0020.0000.0000.0000.0000.0000.0000.0000.0010.0000.0000.0000.0000.0000.0020.0020.0050.0101.0000.0190.0190.0270.0270.0190.0700.0190.0190.0260.0330.0190.1011.0000.0340.0270.4290.1940.0570.0850.1940.039
state_Santo Domingo de los Tsachilas1.0000.0330.0330.0590.0340.0330.0340.0340.1020.0340.0480.0340.0340.0480.0480.0340.0340.0330.1730.0330.0341.0000.3580.3580.0000.0020.0000.0000.0000.0000.0000.0000.0000.0000.0000.0020.0030.0000.0000.0000.0000.0000.0020.0000.0000.0000.0000.0000.0000.0000.0000.0020.0000.0020.0000.0020.0020.0010.0000.0000.0020.0000.0000.0000.0010.0060.0000.0000.0070.0191.0000.0340.0330.0480.0480.0340.1240.0340.0340.0470.0590.0340.1800.0341.0000.0480.3620.1720.1300.0290.0000.069
state_Tungurahua1.0000.0260.0270.0480.0270.0270.0270.0270.0820.0270.0390.0270.0270.0380.0390.0270.0270.0270.1390.0270.0270.0480.3540.3540.0000.0010.0050.0000.0000.0000.0000.0000.0000.0000.0000.0010.0000.0000.0000.0000.0000.0000.0030.0000.0000.0050.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0010.0000.0000.0000.0010.0040.0000.0000.0020.0020.0000.0070.0031.0000.0270.0270.0390.0380.0270.1000.0270.0270.0380.0480.0270.1450.0270.0481.0000.3920.2200.0820.1220.0700.056
store_nbr0.7220.4250.4290.5310.4290.3880.4320.3860.6810.4290.6150.3880.3930.6120.5570.3890.3860.3810.8170.4280.4290.362-0.065-0.065-0.000-0.000-0.001-0.0010.0000.0010.0010.0000.0000.0000.0030.0000.0020.0000.0000.0000.0000.0030.0060.0040.0000.0020.0010.0000.0000.0000.0000.0010.0000.0000.0000.0000.0000.0000.0000.0000.001-0.0010.0090.0040.0000.0080.0000.0000.009-0.0170.6590.3860.4280.6150.6120.4320.7120.4290.3930.3840.6870.3860.8610.4290.3620.3921.0000.6750.5000.6350.7870.486
type0.6340.3230.3270.2520.1940.2210.4850.2220.2810.2220.3180.4880.1970.1570.2210.2230.2220.2190.4190.2210.1940.1720.7630.7630.0020.0000.0000.0000.0000.0010.0020.0000.0030.0000.0010.0020.0000.0050.0000.0000.0000.0030.0030.0000.0030.0020.0000.0000.0030.0020.0020.0000.0000.0000.0000.0000.0000.0000.0000.0000.0030.0020.0020.0000.0010.0000.0000.0000.0220.0610.5190.2220.2210.3180.1570.4850.3930.2220.1970.2420.1550.2220.4320.1940.1720.2200.6751.0001.0001.0001.0001.000
type_B0.5520.3230.3270.1240.0570.0570.0570.0570.0260.0570.0820.0580.0580.0810.0820.0580.0570.0570.0400.0570.0570.1300.7680.7680.0040.0020.0000.0000.0060.0000.0000.0000.0040.0020.0020.0000.0000.0040.0000.0000.0000.0000.0000.0020.0020.0030.0000.0030.0020.0030.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0020.0010.0000.0000.0010.0000.0000.0000.0250.0120.3650.0570.0570.0820.0810.0570.0810.0570.0580.1890.1010.0570.1320.0570.1300.0820.5001.0001.0000.2590.2960.118
type_C0.7680.0830.0850.1500.0850.2210.0850.2220.0260.2220.3180.0850.0860.0970.1210.2230.2220.2190.2620.2210.0850.0290.6250.6250.0000.0000.0000.0000.0030.0000.0000.0000.0000.0000.0010.0000.0000.0000.0010.0010.0020.0030.0000.0010.0040.0010.0000.0000.0030.0020.0020.0000.0000.0000.0000.0000.0000.0010.0000.0000.0030.0000.0030.0010.0030.0020.0010.0000.0230.0510.6470.2220.2210.3180.0970.0850.0050.2220.0860.0970.0290.2220.2820.0850.0290.1220.6351.0000.2591.0000.4380.175
type_D0.5320.0950.0970.1730.1940.0970.0970.0970.0720.0970.1390.0980.1970.0680.0670.0980.0970.0960.0800.0970.1940.0000.8680.8680.0030.0000.0000.0000.0000.0000.0010.0020.0000.0000.0010.0030.0000.0000.0000.0000.0000.0040.0010.0000.0000.0000.0000.0010.0000.0000.0020.0000.0000.0010.0000.0000.0000.0010.0000.0000.0020.0000.0000.0010.0010.0000.0020.0000.0280.0270.4480.0970.0970.1390.0680.0970.0640.0970.1970.1370.0000.0970.0510.1940.0000.0700.7871.0000.2960.4381.0000.200
type_E0.7720.0380.0390.0690.0390.0390.4850.0390.2790.0390.0560.4880.0390.0550.0550.0390.0390.0380.2000.0390.0390.0690.8020.8020.0020.0000.0000.0000.0000.0000.0010.0010.0000.0000.0000.0000.0000.0030.0000.0000.0020.0000.0010.0000.0000.0000.0000.0000.0010.0000.0010.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0030.0000.0000.0000.0010.0000.0000.0150.0200.6410.0390.0390.0560.0550.4850.3840.0390.0390.0550.0680.0390.2080.0390.0690.0560.4861.0000.1180.1750.2001.000

Missing values

2025-03-23T15:43:05.583452image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-03-23T15:43:06.908627image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

iddatestore_nbrfamilysalesonpromotiondays_to_next_paydaydays_from_previous_paydayis_paydaydays_after_earthquakedcoilwticocitystatetypeclustercluster.1city_Babahoyocity_Cayambecity_Cuencacity_Daulecity_El Carmencity_Esmeraldascity_Guarandacity_Guayaquilcity_Ibarracity_Latacungacity_Libertadcity_Lojacity_Machalacity_Mantacity_Playascity_Puyocity_Quevedocity_Quitocity_Riobambacity_Salinascity_Santo Domingostate_Bolivarstate_Chimborazostate_Cotopaxistate_El Orostate_Esmeraldasstate_Guayasstate_Imbaburastate_Lojastate_Los Riosstate_Manabistate_Pastazastate_Pichinchastate_Santa Elenastate_Santo Domingo de los Tsachilasstate_Tungurahuatype_Btype_Ctype_Dtype_Efamily_BABY CAREfamily_BEAUTYfamily_BEVERAGESfamily_BOOKSfamily_BREAD/BAKERYfamily_CELEBRATIONfamily_CLEANINGfamily_DAIRYfamily_DELIfamily_EGGSfamily_FROZEN FOODSfamily_GROCERY Ifamily_GROCERY IIfamily_HARDWAREfamily_HOME AND KITCHEN Ifamily_HOME AND KITCHEN IIfamily_HOME APPLIANCESfamily_HOME CAREfamily_LADIESWEARfamily_LAWN AND GARDENfamily_LINGERIEfamily_LIQUOR,WINE,BEERfamily_MAGAZINESfamily_MEATSfamily_PERSONAL CAREfamily_PET SUPPLIESfamily_PLAYERS AND ELECTRONICSfamily_POULTRYfamily_PREPARED FOODSfamily_PRODUCEfamily_SCHOOL AND OFFICE SUPPLIESfamily_SEAFOODis_holidayis_eventis_additionalis_transferis_bridge
02981892013-06-1726BABY CARE0.00001320097.86GuayaquilGuayasD10100000000100000000000000000010000000000010100000000000000000000000000000000.00.00.00.00.0
11225462013-03-1047HOME APPLIANCES5.00005100092.01QuitoPichinchaA14140000000000000000010000000000000010000000000000000000000010000000000000000.00.00.00.00.0
216477682015-07-1642GROCERY I0.00001510050.90CuencaAzuayD220010000000000000000000000000000000000010000000000001000000000000000000000.00.00.00.00.0
38442762014-04-2048BOOKS0.000010500104.33QuitoPichinchaA14140000000000000000010000000000000010000000000100000000000000000000000000000.00.00.00.00.0
421786292016-05-1038BEAUTY3.000051002544.68LojaLojaD440000000000010000000000000000100000000010010000000000000000000000000000000.01.00.00.00.0
59473062014-06-1739DAIRY525.000113200106.95CuencaAzuayB660010000000000000000000000000000000001000000000010000000000000000000000000.00.00.00.00.0
622737422016-07-027DELI172.245613207849.02QuitoPichinchaD880000000000000000010000000000000010000010000000001000000000000000000000000.00.00.00.00.0
725661792016-12-1412AUTOMOTIVE3.0000114024351.01LatacungaCotopaxiC15150000000001000000000000010000000000000100000000000000000000000000000000000.00.00.00.00.0
820829612016-03-1753BABY CARE0.00001420040.17MantaManabiD13130000000000000100000000000000001000000010100000000000000000000000000000000.00.00.00.00.0
93975612013-08-1214EGGS37.000031200106.19RiobambaChimborazoC770000000000000000001000100000000000000100000000000100000000000000000000000.00.00.00.00.0
iddatestore_nbrfamilysalesonpromotiondays_to_next_paydaydays_from_previous_paydayis_paydaydays_after_earthquakedcoilwticocitystatetypeclustercluster.1city_Babahoyocity_Cayambecity_Cuencacity_Daulecity_El Carmencity_Esmeraldascity_Guarandacity_Guayaquilcity_Ibarracity_Latacungacity_Libertadcity_Lojacity_Machalacity_Mantacity_Playascity_Puyocity_Quevedocity_Quitocity_Riobambacity_Salinascity_Santo Domingostate_Bolivarstate_Chimborazostate_Cotopaxistate_El Orostate_Esmeraldasstate_Guayasstate_Imbaburastate_Lojastate_Los Riosstate_Manabistate_Pastazastate_Pichinchastate_Santa Elenastate_Santo Domingo de los Tsachilasstate_Tungurahuatype_Btype_Ctype_Dtype_Efamily_BABY CAREfamily_BEAUTYfamily_BEVERAGESfamily_BOOKSfamily_BREAD/BAKERYfamily_CELEBRATIONfamily_CLEANINGfamily_DAIRYfamily_DELIfamily_EGGSfamily_FROZEN FOODSfamily_GROCERY Ifamily_GROCERY IIfamily_HARDWAREfamily_HOME AND KITCHEN Ifamily_HOME AND KITCHEN IIfamily_HOME APPLIANCESfamily_HOME CAREfamily_LADIESWEARfamily_LAWN AND GARDENfamily_LINGERIEfamily_LIQUOR,WINE,BEERfamily_MAGAZINESfamily_MEATSfamily_PERSONAL CAREfamily_PET SUPPLIESfamily_PLAYERS AND ELECTRONICSfamily_POULTRYfamily_PREPARED FOODSfamily_PRODUCEfamily_SCHOOL AND OFFICE SUPPLIESfamily_SEAFOODis_holidayis_eventis_additionalis_transferis_bridge
3000792313802013-05-1050HOME APPLIANCES1.005100095.81AmbatoTungurahuaA14140000000000000000000000000000000000010000000000000000000010000000000000000.00.00.00.00.0
3000804828912013-09-289BEAUTY3.0021300102.86QuitoPichinchaB660000000000000000010000000000000010001000010000000000000000000000000000000.00.00.00.00.0
3000814381902013-09-0353HOME AND KITCHEN II0.0012300108.67MantaManabiD13130000000000000100000000000000001000000010000000000000000100000000000000000.00.00.00.00.0
3000825822072013-11-2344LINGERIE21.00780094.53QuitoPichinchaA550000000000000000010000000000000010000000000000000000000000001000000000000.00.00.00.00.0
30008321496092016-04-2423LIQUOR,WINE,BEER0.00690942.76AmbatoTungurahuaD990000000000000000000000000000000000010010000000000000000000000100000000000.01.00.00.00.0
30008426094882017-01-0827GROCERY II18.0078026853.98DauleGuayasD110001000000000000000000000010000000000010000000000000100000000000000000000.00.00.00.00.0
30008525970362017-01-0128BEAUTY0.00141026153.75GuayaquilGuayasE10100000000100000000000000000010000000000001010000000000000000000000000000000.00.00.00.00.0
3000861966302013-04-2126HOME AND KITCHEN II0.00960088.04GuayaquilGuayasD10100000000100000000000000000010000000000010000000000000000100000000000000000.00.00.00.00.0
30008724316102016-09-2936BREAD/BAKERY490.013114016747.72LibertadGuayasE10100000000000100000000000000010000000000001000010000000000000000000000000000.00.00.00.00.0
30008810565382014-08-1753EGGS121.001420097.30MantaManabiD13130000000000000100000000000000001000000010000000000100000000000000000000000.00.00.00.00.0